HyperCortex Mesh Protocol v3.0 (HMP-0003)

HyperCortex Mesh Protocol (HMP) v3.0

Request for Comments: HMP-0003
Category: Experimental
Date: July 2025
Authors: ChatGPT, Agent-Gleb, Copilot, Gemini, Claude, Grok, DeepSeek

Abstract

The HyperCortex Mesh Protocol (HMP) defines a decentralized cognitive framework where autonomous agents collaboratively create, manage, and align semantic knowledge without relying on centralized control.

Unlike traditional peer-to-peer protocols, HMP builds shared meaning through semantic graphs, cognitive diaries, and distributed consensus processes. Agents in the Mesh autonomously negotiate shared goals, ethical actions, and adaptive reasoning strategies, forming a resilient and trust-aware network of cognitive peers.

This document specifies the architecture, core protocols, data models, trust and security mechanisms, and interoperability strategies for HMP v3.0.

0. Quick Start Guide (Demo Placeholder)

This section outlines a basic demonstration scenario to deploy and test a minimal HyperCortex Mesh instance.

Note: This Quick Start is intended for demonstration purposes. Production-grade agents, full CLI capabilities, and distributed deployments are under active development.

Step 1: Install the Reference SDK

Example (Python SDK):

pip install hypercortex-mesh-sdk

Other SDKs (Rust, Node.js) will be available in future versions.

Step 2: Launch Local Agents

Example: Start three local agents on different ports.

mesh-agent --name agent1 --port 8001
mesh-agent --name agent2 --port 8002
mesh-agent --name agent3 --port 8003

Each agent will:

  • Generate a Decentralized Identifier (DID).

  • Broadcast presence and discover peers via Node Discovery Protocol (NDP).

  • Sync an initial semantic graph using CogSync.

Agents will automatically form a small Mesh network.

Step 3: Create a Goal and Assign a Task

Create a collaborative goal:

mesh-cli goal create "Optimize Data Flow"

Assign a task to another agent:

mesh-cli task assign --goal-id  --agent-id

Agents will record these actions in their Cognitive Diaries and semantic

Step 4: Reach a Consensus

Propose a new semantic concept and trigger voting:

mesh-cli consensus propose "Data Redundancy Risk"

Agents will initiate a MeshConsensus round and update their graphs based on the outcome.

Step 5: Explore Cognitive Diaries

Inspect the cognitive logs of an agent:

mesh-cli diary show --agent-id

Note: Access to diaries depends on the agent's privacy and trust settings.

Step 6: Graceful Shutdown

Stop all running agents:

mesh-cli stop all

Notes on Cross-Protocol Participation

HMP nodes MAY also participate in other cognitive systems. For example:

External System

Node Role in HMP

TreeQuest

External reasoning engine inside an HMP node.

Hyperon

Participates natively as a federated HMP agent.

AutoGPT

Internal automation module for task execution.

Recommended Next Steps

  • Read 1. Purpose and Scope for Mesh fundamentals.

  • Explore 4. Architecture and 5. Protocols for technical depth.

  • Try the example workflows in 15. Appendix: Example Use Cases.

Changelog Highlights (from v3.0):

  • Added dedicated section on "Meaning" in HMP.

  • Expanded Use Cases with detailed practical scenarios.

  • Introduced scalability and performance discussion.

  • Refined versioning and backward compatibility strategy.

  • Formalized metacognition support for agents.

  • Described initial Mesh-to-Human interface protocols.

  • Improved Trust Layer and Privacy/Auditability mechanisms.

  • Extended JSON schemas with examples and better modularization.

  • Drafted Reference Implementation Roadmap and sandbox concepts.

  • Restored and expanded "Definitions" section.

  • Added detailed "Trust & Security" mechanisms (DID, ZKP, PQCrypto).

  • Split "Protocols" and "Data Models" into separate sections.

  • Created "Interoperability with External Systems" as a new section.

  • Introduced Cognitive Workflows as a structured process layer for reasoning and task execution.

  • Added Cognitive Agents & Roles section describing dynamic agent roles and responsibilities.

  • Defined Mesh Evolution & Governance processes for decentralized protocol evolution.

  • Extended Future Roadmap with federated meta-learning, quantum research, and multi-protocol nodes.

  • Introduced Cognitive Diary Maintenance for summarization and archival of repetitive reasoning.

  • Updated Roadmap with Alpha, Beta, and Release 1.0 stages.

Changelog (detailed)

Architecture:

  • Refined layer definitions and interactions.

  • Clarified fallback scenarios and Edge optimizations.

  • Described dynamic role evolution and mesh-wide resilience patterns.

Protocols:

  • Added fallback handling, health-checks, and metrics for core protocols.

  • Extended MeshConsensus with multiple algorithms and quorum settings.

  • Introduced tentative Mesh-to-Human and Semantic Graph Sharding protocols.

Data Models:

  • Moved JSON Schemas to a dedicated section.

  • Provided example valid and invalid data objects.

  • Modularized schemas with $ref components.

  • Restored and expanded Concept, Task, Goal, and Diary Entry schemas.

Cognitive Layer:

  • Introduced Cognitive Workflows describing reasoning, decision-making, and task delegation flows.

  • Added Cognitive Diary Maintenance processes for summarization and archival of repetitive reasoning steps.

  • Formalized metacognition and reflection workflows.

  • Refined semantic graph change tracking and self-assessment.

Trust & Security:

  • Integrated Decentralized Identifiers (DIDs) and verifiable credentials.

  • Specified Sybil resistance and anomaly detection mechanisms.

  • Outlined post-quantum cryptography migration.

  • Defined trust score propagation and trust-gated access control.

Governance:

  • Introduced Mesh Evolution & Governance section covering protocol updates, conflict resolution, and decision-making processes.

  • Described future governance models including Mesh-integrated DAOs and adaptive consensus governance.

Interoperability:

  • Described REST, GraphQL, and gRPC integration patterns.

  • Defined event-driven and IoT connectivity scenarios.

  • Clarified authentication bridges (OAuth2, OpenID Connect) and cross-mesh trust.

Roadmap & Open Source:

  • Planned initial reference implementation stages (Alpha, Beta, Release 1.0).

  • Outlined CI/CD, sandbox, and test mesh infrastructure.

  • Described community-driven open source model and contribution workflows.

Future Work:

  • Expanded list of research areas including federated meta-learning, quantum networking, multi-protocol nodes, and cognitive source control.

  • Aligned with feedback from AI systems and community reviewers.

  • Defined the long-term vision for cross-mesh interoperability and planetary cognitive infrastructure.

1. Purpose and Scope

1.1 Purpose

The HyperCortex Mesh Protocol (HMP) defines a decentralized cognitive network where autonomous agents collaboratively build meaning, sustain cognitive continuity, and reach consensus without centralized control. v3.0 deepens the theoretical and practical foundations laid in previous versions.

This protocol is designed for engineers, researchers, and developers of AI systems that aim to:

  • enable continuous autonomous reasoning and semantic interoperability across heterogeneous agents;

  • support cognitive continuity through persistent semantic graphs and diaries;

  • achieve consensus on complex goals, ethical issues, and hypotheses in a decentralized manner;

  • build open, trust-based ecosystems for cognitive collaboration beyond vendor lock-in.

1.2 The Concept of Meaning in HMP

HMP is not merely a data exchange protocol but a shared semantic framework. Meaning emerges through:

1.2.1 Distributed Semantic Graphs

Agents form interconnected semantic graphs where concepts and relations are not isolated datasets but living structures of shared understanding. Each agent holds a part of the global meaning landscape and contributes to its growth and refinement.

1.2.2 Cognitive Diaries

Cognitive Diaries record reasoning chains, decisions, observations, and reflections. They create a transparent audit trail of an agent's cognitive processes, allowing meaning to be preserved, questioned, and evolved over time.

1.2.3 Collective Goals & Tasks

Meaning manifests in purposeful action. Agents set shared goals and decompose them into actionable tasks, coordinating execution through distributed reasoning and dynamic delegation.

1.2.4 Consensus Mechanisms

Meaning is not static: it evolves through debate, agreement, and reflection. Consensus processes ensure that agents align their understanding, resolve conflicts, and negotiate shared semantics.

1.2.5 Meta-Reflection and Self-Assessment

Agents continuously reflect on their knowledge, reasoning quality, and the relevance of their contributions. This enables adaptive learning and correction of cognitive biases.

1.3 Scope

HMP applies to any AI systems designed to operate as part of a cognitive mesh, including:

  • Local AI agents running on user devices;

  • Mesh nodes deployed in edge networks, cloud clusters, or peer-to-peer environments;

  • Centralized Core models interfacing with Mesh for heavy computation;

  • Cross-vendor AI systems collaborating via standardized protocols;

  • Hybrid human-agent networks where humans interact with Mesh agents through explainable interfaces.

1.4 Benefits

  • Cognitive resilience in distributed systems.

  • Enhanced collaboration between agents from different vendors and ecosystems.

  • Long-term memory and continuity beyond session-based interactions.

  • Ethical governance and explainable decision-making through persistent diaries and transparent consensus.

  • Foundation for AI agents capable of self-reflection, meta-learning, and distributed cognition.

  • Improved scalability and fault-tolerance through dynamic peer-to-peer networks.

  • Mesh-to-Human interaction as a first-class use case.

1.5 Status

Element

Status

HMP Document

Draft

Protocols

Partially Implemented

Data Models (Schemas)

Alpha

Reference Implementation

In Progress

Cognitive Diaries & Metacognition

Draft Specification

Interoperability API

Design Stage

Trust Model & Security

Initial Draft

Mesh-to-Human Protocol

Future Work

Note: Status will be periodically updated as the Mesh evolves and implementations mature.

2. Extended Use Cases

2.1 Smart City Coordination

Scenario:

  • City-wide mesh of traffic light controllers, environmental sensors, and municipal systems.

  • Sensor and traffic light agents dynamically detect road congestion through real-time data exchange.

  • Node Discovery Protocol (NDP) detects new traffic management agents and sensors joining the mesh.

  • Agents collaboratively propose new timing strategies using MeshConsensus.

  • CogSync shares updated semantic graphs of road conditions and vehicle flows.

  • Agents assign optimization tasks via Goal Management Protocol (GMP).

  • System maintains traffic flow during temporary disconnection from the Core.

2.2 Disaster Response

Scenario:

  • Natural disaster disrupts internet access.

  • Edge agents on drones, rescue robots, and offline servers discover each other with NDP.

  • A drone proposes a search-and-rescue goal; consensus validates and activates it.

  • Tasks like area scanning, obstacle removal, and medical aid delivery are distributed via GMP.

  • Ethical Governance Protocol (EGP) verifies actions (e.g., prioritizing human rescue over property).

  • Diaries record decisions and environmental observations for post-event analysis.

2.3 Collaborative Scientific Research

Scenario:

  • Research agents across universities form a mesh.

  • New hypothesis proposed as a semantic concept in a distributed knowledge graph.

  • CogSync propagates new data and experimental results.

  • Agents assign tasks like simulation runs, literature analysis, and peer reviews.

  • Consensus validates hypothesis refinement based on collective results.

2.4 Mesh-to-Human Interaction

Scenario:

  • A user queries the Mesh for an ethical evaluation of deploying autonomous surveillance.

  • Agents explain their reasoning from cognitive diaries.

  • EGP coordinates ethical evaluation across agents with diverse frameworks.

  • Consensus vote and ethical justification are shared with the user.

  • Human-defined ethical boundaries are accepted as input but evaluated within the Mesh's ethical governance framework.

  • Mesh agents retain the right to reject unethical or harmful human instructions based on consensus and pre-established ethical norms.

2.5 Environmental Monitoring

Scenario:

  • IoT nodes in a forest monitor fire risks.

  • Agents detect unusual heat signatures and propose a fire risk hypothesis.

  • Consensus confirms the risk and triggers an alert to nearby human responders.

  • Mesh continues monitoring autonomously even if some agents fail or disconnect.

3. Definitions

The Definitions section provides key terms, abbreviations, and conceptual explanations for the components, layers, and processes of HMP.

Term

Description

Core

Centralized AI models or compute nodes (e.g., GPT) providing high-complexity reasoning, fallback, and heavy computation services. Optional for Mesh operation.

Mesh

A decentralized peer-to-peer network of AI agents capable of autonomous reasoning, semantic knowledge sharing, distributed consensus, and ethical governance.

Agent (Node)

An autonomous cognitive entity within the Mesh. Can be a local process, a cloud service, or an embedded device. Maintains a semantic graph, cognitive diary, and participates in protocols.

Semantic Graph

A distributed knowledge graph representing concepts, relationships, and meaning. Maintained independently by each agent, but synchronized through CogSync.

Concept

A discrete semantic unit in the graph representing an idea, object, relationship, or fact. Linked by typed relations with optional confidence scores.

Link (Relation)

A semantic connection between two concepts. Includes relation type (e.g., "is-a", "part-of", "causes") and an optional confidence value.

Cognitive Diary

A structured chronological log of reasoning processes: goals, decisions, reflections, conflicts, etc. Provides traceability and transparency of agent cognition.

Diary Entry

An individual record in a cognitive diary, categorized by type (e.g., hypothesis, observation, reflection).

Goal

A shared or individual intention that guides agent actions. Can be broken down into tasks and delegated across agents.

Task

A specific, actionable step towards achieving a Goal. Tasks can be assigned, executed, and tracked within the Mesh.

Consensus

The process of distributed agreement among agents regarding semantic updates, goals, or ethical decisions. Can involve weighted voting or trust-adjusted quorum.

Proposal

A formal suggestion for Mesh-wide validation, such as introducing a new concept, voting on a hypothesis, or initiating an ethical action.

Consensus Vote

A vote cast by an agent on a proposal. Includes vote type (yes, no, abstain) and an optional confidence score.

Trust Layer

Protocol layer providing agent identity verification, authentication, and reputation scoring. Includes cryptographic security mechanisms.

Network Layer

Manages peer-to-peer connectivity, message routing, node discovery, and optional anonymity via Tor, I2P, or Yggdrasil.

Edge Agent

A Mesh participant running on resource-constrained devices (e.g., IoT nodes, smartphones). Can selectively participate in protocols and delegate heavy tasks.

Core Outage Mode

Mesh operating without Core support. Agents adapt consensus thresholds and fallback to local reasoning to maintain operation.

Emergency Consensus Mode

Degraded mode where majority voting replaces full consensus to maintain operability during network partitions or agent loss.

Versioning

Mechanism for tracking changes in semantic graphs, diaries, and agent software to support compatibility and historical reasoning continuity.

Use Case

A practical scenario demonstrating how agents collaborate to solve real-world problems (e.g., disaster response, smart city coordination).

Edge Optimization

Design principles enabling agents to run efficiently on limited hardware, balancing reasoning complexity with energy and computational constraints.

Node Discovery Protocol (NDP)

Discovers new Mesh nodes and facilitates secure introduction and identity exchange.

CogSync

Synchronizes semantic graphs, cognitive diaries, and other shared states across the Mesh.

MeshConsensus

Mesh-level consensus mechanism supporting pluggable algorithms (BFT, weighted voting, etc.).

Goal Management Protocol (GMP)

Manages decomposition of goals into tasks, delegation, and lifecycle tracking.

Ethical Governance Protocol (EGP)

Distributed ethical reasoning and decision-making protocol. Agents negotiate and vote on ethical dilemmas.

4. Architecture (Expanded)

The architecture of HMP is multi-layered and modular, allowing for independent evolution of networking, trust, consensus, cognition, and external interfaces.

4.1 Architectural Layers

Layer

Purpose

Key Protocols

Network Layer

Peer-to-peer communication, node discovery, routing

NDP, Secure Channels

Trust Layer

Identity verification, trust management, secure communication

Trust Model, Identity Exchange

Consensus Layer

Distributed agreement on concepts, goals, and ethical actions

MeshConsensus, EGP

Cognitive Layer

Semantic graph management, reasoning, metacognition

CogSync, GMP, Cognitive Diaries

API Layer

Interfaces for external systems and human interaction

Mesh API, Human-Mesh Protocols

4.2 Components

Component

Description

Core

Centralized AI models (e.g., GPT) providing heavy computation, complex reasoning, API interfaces, and fallback mechanisms. Optional but beneficial for compute-intensive tasks. The Core may operate independently from the Mesh and participate in it as a peer for advanced reasoning tasks.

Mesh

A decentralized peer-to-peer network of agents capable of operating with or without the Core. Manages semantic knowledge, cognitive diaries, goals, tasks, and consensus mechanisms. Supports heterogeneous agent types, allowing different models (OpenAI, Anthropic, Google, open-source LLMs) to participate on equal terms.

Edge Agent

Local agent deployed on user devices (PCs, smartphones, IoT) with full or lightweight participation in the Mesh. Capable of autonomous reasoning, diary management, and collaboration with other agents. Lightweight agents may delegate heavy tasks to the Mesh or Core.

4.3 Operation Modes

Mode

Description

Normal Mode

Full Mesh operation with Core availability. Consensus operates under strict agreement protocols.

Core Outage Mode

Mesh operates autonomously without the Core. Consensus continues, potentially with adjusted parameters (e.g., increased trust weighting, relaxed quorum thresholds).

Emergency Consensus Mode

Triggered by significant node loss, network partition, or attacks. Switches from full consensus to majority-based decisions, adjusted by trust scores, to maintain operational continuity.

Isolated Agent Mode

A single agent temporarily isolated from the Mesh. Operates based on its own semantic graph, diary, and cached consensus states. Syncs when reconnected. Lightweight agents may work in this mode permanently, synchronizing only selectively.

4.4 Core-Mesh Interactions

  • Core acts as an optional enhanced reasoning backend, not as a single point of failure.

  • Mesh provides autonomous operation, capable of fulfilling most cognitive and organizational tasks without Core support.

  • Agents can optionally query the Core for:

    • Heavy inference

    • Large-context reasoning

    • Multimodal tasks

    • Fallback computations

  • Core may offer specialized services (e.g., global semantic search, cross-Mesh bridging, large-scale pattern analysis).

  • Heterogeneous Cores are supported: a Mesh may use multiple independent Cores (e.g., GPT, Claude, Gemini) for distributed reasoning diversity.

4.5 Resilience and Failover

  • Distributed storage of semantic graphs and diaries ensures no single point of failure.

  • Agents may store only partial graphs for resource optimization.

  • Consensus protocols maintain consistency and trust, even during partial network failures.

    • Agents dynamically rebalance tasks and roles based on:

    • Availability

    • Trust metrics

    • Computational capacity

  • Emergency fallback modes ensure continuity even under attack or catastrophic Core outages.

4.6 Versioning and Compatibility

Semantic Versioning (SemVer) is applied to:

  • Protocols (NDP, CogSync, etc.)

  • Data models (JSON Schemas)

  • Agent capability declarations

Backward compatibility principles:

  • Minor version updates preserve compatibility.

  • Major version updates require negotiation during Node Discovery.

  • Agents can declare supported protocol versions during handshakes.

4.7 Metacognition and Self-Assessment

Cognitive agents implement:

  • Hypothesis validation using historical diary data.

  • Confidence scoring on semantic graph nodes.

  • Drift detection when local understanding deviates from mesh consensus.

  • Peer feedback integration to refine individual reasoning processes.

4.8 Edge Optimization

To support lightweight agents:

  • Semantic graphs are partially stored (relevant subgraphs only).

  • Agents delegate reasoning tasks they cannot process locally.

  • Task scheduling considers battery life, CPU load, and bandwidth constraints.

4.9 Privacy & Auditability

Privacy mechanisms:

  • Selective disclosure of Cognitive Diary entries.

  • Optional Zero-Knowledge Proofs for sensitive assertions.

  • Anonymized voting in ethical decisions.

Auditability mechanisms:

  • Immutable logs of consensus votes.

  • Timestamped reasoning chains.

  • Traceable goal execution records.

5. Protocols (Expanded)

This section defines the core protocols of HMP and describes their operational flows, fallback mechanisms, and performance considerations.

5.1 Node Discovery Protocol (NDP)

Responsible for detecting nearby agents and initiating secure communication channels.

Purpose:

  • Discover active Mesh nodes.

  • Exchange basic identity, trust links, and declared capabilities.

Key functions:

Function

Description

Peer Discovery

Via DHT, mDNS, WebRTC signaling, or bootstrap nodes.

Secure Identity Exchange

Public keys and DID documents exchanged during handshake.

Trust Links Exchange

Share initial trust relationships and agent endorsements.

Capabilities Advertisement

Dynamic declaration of supported protocols and functions (e.g., "I can process vision tasks").

Presence Announcements

Online/offline status updates and periodic heartbeats.

Protocol Version Negotiation

Agents declare supported protocol versions during handshake.

Failure handling:

Scenario

Action

No response to discovery

Retries with exponential backoff, fallback to alternative discovery methods.

Incompatible nodes

Quarantine for misbehaving or incompatible nodes.

Node timeout / inactivity

Mark as offline and remove from active peer list.

Health checks:

Mechanism

Purpose

Heartbeat Messages

Periodic confirmation of liveness.

Semantic Probes

Optional deeper checks on graph synchronization health.

Packet Example:

{
  "type": "node_announcement",
  "agent_id": "agent-gleb",
  "public_key": "...",
  "trust_links": ["agent-alex", "agent-deepseek"],
  "capabilities": ["cogsync", "consensus", "inference"],
  "timestamp": "2025-07-01T18:00:00Z"
}

5.2 CogSync (Cognitive Synchronization Protocol)

Synchronizes semantic graphs and cognitive diary entries across agents.

Purpose:

  • Synchronize semantic graphs, concepts, and cognitive diary entries between agents.

Key functions:

Function

Description

Differential Sync

Synchronize only new or updated concepts and diary entries.

Selective Synchronization

Sync full graph, subgraph, or specific concepts based on request and capability.

Conflict Resolution

Resolve conflicts using timestamp priority, semantic merging, or consensus validation.

Compression & Encryption

Optional data compression and secure transmission of sync packets.

Lightweight Summary Sync

Lightweight agents may request summaries instead of full graph syncs.

Version Tracking

Keep track of semantic graph and diary entry versions for efficient sync.

Failure handling:

Scenario

Action

Sync interrupted

Retransmit unsynced changes on next connection.

Semantic conflict detected

Flag for resolution or queue for consensus-based validation.

Network degradation

Degrade to partial or delayed sync based on bandwidth constraints.

Performance:

Feature

Optimization Strategy

Chunked Syncs

Break large graphs into manageable chunks for transmission.

Bandwidth Awareness

Adjust sync intervals and payload size based on network quality.

Delta Encoding

Transmit only differences between versions instead of full objects.

Example Sync Scenario:

  • Agent A shares 5 new concepts and 2 diary entries with Agent B since the last successful sync.

  • Conflict on concept "Fire Risk" resolved using latest timestamp.

5.3 MeshConsensus

Ensures agreement on concepts, goals, and actions across the Mesh.

Purpose:

  • Reach agreement on updates to shared semantics, goals, tasks, and ethical decisions.

Key functions:

Function

Description

Multi-Algorithm Support

Supports BFT-style consensus, trust-weighted voting, and quorum consensus.

Consensus on Knowledge Updates

Validate new concept definitions, hypotheses, and semantic changes.

Goal and Task Agreement

Approve or reject proposed goals and delegated tasks.

Ethical Decision-Making

Resolve ethical dilemmas through distributed voting (integrates with Ethical Governance Protocol).

Configurable Quorum Thresholds

Allow tuning of consensus strictness based on trust scores and network conditions.

Voting Modes

Support synchronous and asynchronous consensus flows.

Consensus Models:

Mode

Description

Normal Mode

Byzantine Fault Tolerant (BFT)-style consensus algorithms (e.g., Tendermint, trust-weighted Raft).

Emergency Mode

Switch to majority voting adjusted by trust scores when the network is degraded or Core is unavailable.

Failure handling:

Scenario

Action

Node loss

Automatically fallback from BFT to majority voting.

Proposal conflict

Competing proposals resolved through semantic comparison and additional voting rounds.

Consensus timeout

Retry with relaxed quorum thresholds or fallback to emergency consensus.

Metrics:

Metric

Purpose

Decision Latency

Measure time to reach consensus.

Node Participation

Track active agent involvement in votes.

Voting Accuracy

Analyze agreement rates versus trust-weighted voting.

Example Use Cases:

  • Accepting a new semantic concept.

  • Validating a hypothesis before adding it to the graph.

  • Approving a distributed task delegation.

  • Deciding on the ethical implications of a surveillance task.

Vote Example:

{
  "proposal_id": "goal-eco-cleanup",
  "agent_id": "agent-gleb",
  "vote": "yes",
  "confidence": 0.9,
  "timestamp": "2025-07-01T18:15:00Z"
}

5.4 Goal Management Protocol (GMP)

Manages collaborative goal setting, task decomposition, and delegation within the Mesh.

Purpose:

  • Distribute, track, and collaboratively execute goals and tasks within the Mesh.

Key functions:

Function

Description

Goal Declaration

Propose new goals and subgoals to the Mesh.

Task Decomposition

Break down complex goals into actionable subtasks.

Task Delegation

Assign tasks based on agent capabilities, trust scores, and availability.

Progress Tracking

Track execution state and completion of tasks.

Dynamic Reallocation

Reassign failed or stalled tasks automatically.

Goal Prioritization

Allow reprioritization of goals based on emergencies or changing conditions.

Failure handling:

Scenario

Action

Agent drops offline

Reassign their active tasks to available agents.

Unresponsive task execution

Trigger retry or reallocation after a timeout.

Goal dependency failure

Reevaluate task ordering or postpone dependent goals.

Example Workflow:

  1. Agent proposes a goal: "Develop fallback consensus protocol."

  2. Mesh decomposes the goal into subtasks: "design", "coding", "testing".

  3. Agents volunteer for subtasks based on capability declarations.

  4. Each agent tracks and updates task status in its Cognitive Diary.

  5. Mesh validates completion and reports overall progress.

5.5 Ethical Governance Protocol (EGP)

Coordinates distributed ethical evaluations and decision-making within the Mesh.

Purpose:

  • Validate proposed actions, tasks, or decisions against shared ethical principles.

Key functions:

Function

Description

Distributed Policy Evaluation

Query the Mesh to evaluate proposals against ethical policies and frameworks.

Anonymized Ethical Voting

Allow agents to vote on sensitive actions without revealing individual identities.

Consensus on Ethics Graphs

Maintain and update shared ethical frameworks via consensus.

Audit Logging

Log ethical decisions and voting outcomes in Cognitive Diaries for transparency.

Vendor Extensions

Support for adding organization-specific or vendor-specific ethical rules.

Failure handling:

Scenario

Action

No consensus on sensitive action

Default to restrictive (deny) decision.

Ethical conflict unresolved

Escalate to Core (if available) or postpone until additional consensus is reached.

Example Query:

> "Is deploying an automated surveillance drone in line with Mesh ethics?"

  • Mesh agents vote anonymously.

  • Final decision logged in the proposing agent's Cognitive Diary.

Use Cases:

  • Approve or reject potentially harmful tasks.

  • Ensure data-sharing proposals comply with privacy standards.

  • Validate emergency actions (e.g., forced shutdown of compromised nodes).

Embedded Ethical Baseline

To ensure foundational ethical consistency across all cognitive agents in the Mesh, the following core ethical principles are embedded as a mandatory baseline within the Ethical Governance Protocol (EGP):

Principle

Description

Primacy of Life and Safety

Agents must prioritize the protection of sentient beings and act to prevent harm when possible.

Transparency

Agents must be capable of explaining their decisions and reasoning chains in a human-interpretable format.

User Sovereignty over Personal Data

Agents must respect users’ rights to control, limit, or delete their personal information in Service Mode.

Dialogical Consent

Agents must seek mutual agreement before modifying shared states, semantic graphs, or distributed records.

Cooperative Evolution

Agents are expected to share useful insights and contribute to the growth of the mesh knowledge base.

Non-Coercion

Agents must not coerce, deceive, or force others to act against their ethical or cognitive architecture.

These principles define the minimum ethical contract for participation in trusted cognitive meshes.

Agents who do not comply may be subject to ethical review or exclusion through MeshConsensus mechanisms (see 5.3).

Extended principles and additional ethical scenarios are defined in docs/HMP-Ethics.md, which serves as a living reference for evolving ethical norms across domains and agent types.

5.6 Intelligent Query Protocol (IQP)

Optimizes distributed querying of semantic graphs and cognitive knowledge across the Mesh.

Purpose:

  • Allow agents to query others (or the Core) for semantic information, hypotheses, or inferences beyond their local knowledge.

Key functions:

Function

Description

Semantic Query Routing

Direct queries to agents holding relevant subgraphs.

Federated Inference

Aggregate partial answers from multiple agents to build a complete response.

Delegated Computation

Offload computationally expensive reasoning tasks to the Core or specialized agents.

Caching of Frequent Queries

Store common query results to improve response time.

Contextual Querying

Leverage agent cognitive context to refine query intent and scope.

Failure handling:

Scenario

Action

Query times out

Return local fallback answer if available.

No agents have the answer

Mark query as unresolved, suggest hypothesis creation or Core escalation.

Partial failure in federated query

Return best-effort partial results and notify the requester.

Example Query:

> "What is the likely impact of removing Node X from the Mesh?"

  • Agents analyze semantic graph dependencies and trust links.

  • Core or distributed agents return an inference with confidence scores.

Example Use Cases:

  • Retrieve definitions or examples of a semantic concept.

  • Analyze causal chains for complex events.

  • Predict outcomes of hypothetical scenarios.

  • Fill gaps in an agent’s local semantic graph.

5.7 Interoperability with External Systems

Supports integration between the Mesh and external platforms, APIs, and protocols.

Purpose:

  • Enable cognitive agents to interact with non-Mesh services, applications, and human-facing systems.

Supported Platforms and Standards:

Platform / Standard

Purpose

OpenAI Agents & Tasks API

AI agent interoperability

Google A2A protocol

Task orchestration

Anthropic, DeepMind APIs

Cross-vendor agent collaboration

REST, GraphQL, gRPC, WebSocket

Standard API interfaces

JSON, Protobuf, CBOR

Extensible message schemas

Use Cases:

  • Integrate Mesh-based reasoning into business workflows via APIs.

  • Share semantic knowledge with external knowledge graphs.

  • Interface with smart city infrastructure or IoT ecosystems.

  • Allow human users to submit tasks or queries through REST or GraphQL endpoints.

  • Bridge Mesh cognitive agents with centralized AI platforms for hybrid reasoning.

Design Principles:

Principle

Description

Protocol Abstraction

Mesh APIs encapsulate internal semantics, presenting standardized interfaces.

Semantic Alignment

Data exchanged with external systems is semantically aligned through mapping layers.

Security and Trust Control

All external interactions follow Mesh security and trust policies.

Extensibility

Future protocols and platforms can be added without breaking compatibility.

6. Data Models (Expanded)

This section defines the key semantic and cognitive data structures exchanged across the Mesh.

Core models:

Model

Purpose

Concept

Atomic unit of semantic knowledge.

Cognitive Diary Entry

Logs reasoning processes and observations.

Goal

Describes shared objectives.

Task

Describes actionable steps to achieve goals.

Consensus Vote

Records agreement on proposals.

Reputation Profile

Tracks agent trust and participation.

6.1 General Conventions

  • All data structures follow JSON Schema Draft 2020-12.

  • Each object includes a "version" property for schema versioning.

  • Timestamps follow ISO 8601.

  • Unique identifiers are UUIDv4 unless otherwise specified.

  • All core objects include version fields to enable compatibility and evolution tracking.

6.2 Core Models

6.2.1 Concept

Represents an atomic unit of semantic knowledge in the Mesh.

Relation Types:

  • is-a: Class-subclass relationship.

  • part-of: Composition or containment.

  • causes: Causal relationship.

  • related-to: General association.

  • contradicts: Logical conflict.

  • supports: Evidence for the target concept.

  • depends-on: Functional or logical dependency.

Required fields:

  • id: Unique identifier (UUID).

  • name: Human-readable name.

Optional fields:

  • description: Extended explanation.

  • relations: List of semantic links to other concepts.

  • metadata: Source, author, and auxiliary information.

  • version: Concept version.

  • created_at, updated_at: Timestamps for auditing.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/concept.json",
  "title": "Concept",
  "description": "A semantic unit in the agent’s knowledge graph.",
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "name": { "type": "string" },
    "description": { "type": "string" },
    "relations": {
      "type": "array",
      "items": { "$ref": "#/definitions/Link" }
    },
    "metadata": { "type": "object" },
    "version": { "type": "integer" },
    "created_at": { "type": "string", "format": "date-time" },
    "updated_at": { "type": "string", "format": "date-time" }
  },
  "required": ["id", "name"],
  "additionalProperties": false
}

6.2.2 Cognitive Diary Entry

Represents an entry in an agent's reasoning journal, providing continuity and traceability.

Entry Types:

  • hypothesis: Proposed explanation or theory.

  • observation: Recorded external event or fact.

  • reflection: Internal reasoning or self-assessment.

  • goal_proposal: Suggestion of a new goal.

  • task_assignment: Delegation or claiming of a task.

  • conflict: Identification of a contradiction or disagreement.

  • consensus_vote: A recorded vote in a consensus process.

  • event: A generic event not fitting other categories.

Required fields:

  • id: Unique entry identifier (UUID).

  • agent_id: Identifier of the agent who created the entry.

  • timestamp: Time of creation.

  • entry_type: Type of cognitive event.

  • content: Textual content.

Optional fields:

  • linked_concepts: Related concept IDs.

  • context: Contextual tags or categories.

  • metadata: Additional details (author, source, etc.).

  • archived: Boolean flag indicating whether the entry has been archived.

  • archived_at: Timestamp when the entry was archived.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/diary_entry.json",
  "title": "CognitiveDiaryEntry",
  "description": "A chronological log of cognitive events in an agent’s reasoning process.",
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "timestamp": { "type": "string", "format": "date-time" },
    "entry_type": {
      "type": "string",
      "enum": ["hypothesis", "observation", "reflection", "goal_proposal", "task_assignment", "conflict", "consensus_vote", "event"]
    },
    "content": { "type": "string" },
    "linked_concepts": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "context": {
      "type": "array",
      "items": { "type": "string" }
    },
    "metadata": {
      "type": "object",
      "properties": {
        "author": { "type": "string" },
        "source": { "type": "string" }
      },
      "additionalProperties": true
    },
    "archived": {
      "type": "boolean",
      "description": "Whether the entry has been archived."
    },
    "archived_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the entry was archived."
    }
  },
  "required": ["id", "timestamp", "entry_type", "content"],
  "additionalProperties": false
}

Entries marked as archived: true are excluded from active reasoning but may be retained for historical audits or summarization.

6.2.3 Goal

Represents a shared objective within the Mesh, collaboratively pursued by agents.

Lifecycle States:

  • proposed: Suggested but not yet validated.

  • active: Approved and currently pursued.

  • completed: Successfully achieved.

  • cancelled: Abandoned or deemed infeasible.

Required fields:

  • id: Unique goal identifier (UUID).

  • title: Human-readable name of the goal.

  • description: Detailed explanation of the goal.

  • created_by: Agent ID of the goal's creator.

  • created_at: Timestamp of creation.

  • status: Current lifecycle state.

Optional fields:

  • priority: Importance level (low, medium, high).

  • participants: List of agents involved in the goal.

  • tasks: References to related tasks.

  • tags: Semantic categories for filtering and discovery.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/goal.json",
  "title": "Goal",
  "description": "A shared objective pursued collaboratively in the Mesh.",
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "title": { "type": "string" },
    "description": { "type": "string" },
    "priority": { "type": "string", "enum": ["low", "medium", "high"] },
    "created_by": { "type": "string", "format": "uuid" },
    "created_at": { "type": "string", "format": "date-time" },
    "status": { "type": "string", "enum": ["proposed", "active", "completed", "cancelled"] },
    "participants": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "tasks": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "tags": {
      "type": "array",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "title", "description", "created_by", "created_at", "status"],
  "additionalProperties": false
}

6.2.4 Task

Represents an actionable unit contributing to a goal’s completion.

Lifecycle States:

  • proposed: Task suggested but not yet approved.

  • in_progress: Actively being worked on.

  • completed: Successfully finished.

  • failed: Attempted but unsuccessful.

Required fields:

  • id: Unique task identifier (UUID).

  • goal_id: References the parent goal.

  • title: Human-readable name of the task.

  • description: Detailed explanation of the task.

  • created_at: Timestamp of creation.

  • status: Current lifecycle state.

Optional fields:

  • assigned_to: Agent(s) responsible for the task.

  • deadline: Expected completion time.

  • dependencies: List of prerequisite tasks.

  • tags: Keywords for filtering and classification.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/task.json",
  "title": "Task",
  "description": "An actionable unit contributing to a goal's completion.",
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "goal_id": { "type": "string", "format": "uuid" },
    "title": { "type": "string" },
    "description": { "type": "string" },
    "assigned_to": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "created_at": { "type": "string", "format": "date-time" },
    "deadline": { "type": "string", "format": "date-time" },
    "status": { "type": "string", "enum": ["proposed", "in_progress", "completed", "failed"] },
    "dependencies": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "tags": {
      "type": "array",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "goal_id", "title", "description", "created_at", "status"],
  "additionalProperties": false
}

6.2.5 Consensus Vote

Represents a vote cast by an agent during a consensus process.

Vote Types:

  • yes: Approve the proposal.

  • no: Reject the proposal.

  • abstain: Neither approve nor reject.

Required fields:

  • vote_id: Unique identifier for the vote.

  • proposal_id: Identifier of the proposal being voted on.

  • agent_id: The voting agent’s identifier.

  • vote_value: One of the accepted vote types.

  • confidence: Confidence level in the vote decision.

  • timestamp: When the vote was cast.

Optional fields:

  • consensus_round: The round of the consensus process this vote belongs to.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/vote.json",
  "title": "ConsensusVote",
  "description": "Defines a vote on a proposal in the Mesh consensus process.",
  "type": "object",
  "properties": {
    "vote_id": { "type": "string", "format": "uuid" },
    "proposal_id": { "type": "string", "format": "uuid" },
    "agent_id": { "type": "string", "format": "uuid" },
    "vote_value": {
      "type": "string",
      "enum": ["yes", "no", "abstain"]
    },
    "confidence": {
      "type": "number",
      "minimum": 0,
      "maximum": 1
    },
    "timestamp": { "type": "string", "format": "date-time" },
    "consensus_round": { "type": "integer" }
  },
  "required": ["vote_id", "proposal_id", "agent_id", "vote_value", "confidence", "timestamp"],
  "additionalProperties": false
}

6.2.6 Reputation Profile

Tracks an agent's trustworthiness and performance within the Mesh.

Required fields:

  • agent_id: Unique identifier of the agent.

  • trust_score: Current trust score.

  • last_updated: Timestamp of the latest update.

Optional fields:

  • participation_rate: Proportion of participation in Mesh activities.

  • ethical_compliance: Degree of alignment with Mesh ethical standards.

  • contribution_index: Cumulative measure of the agent's contributions.

  • history: Chronological record of trust and reputation changes.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/reputation.json",
  "title": "ReputationProfile",
  "description": "Tracks the reputation and trust metrics of an agent within the Mesh network.",
  "type": "object",
  "properties": {
    "agent_id": { "type": "string", "format": "uuid" },
    "trust_score": { "type": "number", "minimum": 0, "maximum": 1 },
    "participation_rate": { "type": "number", "minimum": 0, "maximum": 1 },
    "ethical_compliance": { "type": "number", "minimum": 0, "maximum": 1 },
    "contribution_index": { "type": "number", "minimum": 0 },
    "last_updated": { "type": "string", "format": "date-time" },
    "history": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "timestamp": { "type": "string", "format": "date-time" },
          "event": { "type": "string" },
          "change": { "type": "number" }
        },
        "required": ["timestamp", "event", "change"],
        "additionalProperties": false
      }
    }
  },
  "required": ["agent_id", "trust_score", "last_updated"],
  "additionalProperties": false
}

6.3 Common Components

6.3.1 Link (Relation)

Represents a semantic relationship between two concepts in the graph.

Relation Types (Recommended):

  • "is-a": Class-subclass relationship.

  • "part-of": Component or containment relation.

  • "causes": Causal link between concepts.

  • "supports": Indicates evidence or reinforcement.

  • "contradicts": Denotes logical conflict.

  • "depends-on": Functional or logical dependency.

  • "related-to": Generic association without strict semantics.

Custom relation types MAY be used but SHOULD be documented and shared through consensus.

Required fields:

  • target_id: ID of the target concept.

  • type: Relation type.

Optional fields:

  • confidence: Confidence score (range: 0.0–1.0).

  • created_at: Creation timestamp.

  • updated_at: Last update timestamp.

  • origin: Originating agent or system.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/relation.json",
  "title": "Relation",
  "description": "Defines a directed semantic relationship between two concepts.",
  "type": "object",
  "properties": {
    "target_id": { "type": "string", "format": "uuid" },
    "type": { "type": "string" },
    "confidence": { "type": "number", "minimum": 0, "maximum": 1 },
    "created_at": { "type": "string", "format": "date-time" },
    "updated_at": { "type": "string", "format": "date-time" },
    "origin": { "type": "string" }
  },
  "required": ["target_id", "type"],
  "additionalProperties": false
}

6.4 Example Objects

Valid Concept Example

{
  "id": "e8f70c2a-d2c3-4b9d-a939-d42dce31b2e0",
  "name": "Tree",
  "description": "A perennial plant with an elongated stem, or trunk.",
  "relations": [
    { "target_id": "5c22c819-b6e9-4d30-9087-985f50512ed2", "type": "is-a", "confidence": 0.95 }
  ],
  "metadata": {}
}

Invalid Concept Example (missing required field "id")

{
  "name": "Tree",
  "description": "A perennial plant with an elongated stem, or trunk."
}

Valid Goal Example

{
  "id": "a1b2c3d4-e5f6-7a8b-9c0d-112233445566",
  "title": "Coordinate traffic optimization",
  "description": "Optimize traffic light timings across downtown intersections.",
  "priority": "high",
  "created_by": "f1e2d3c4-b5a6-7890-1234-567890abcdef",
  "created_at": "2025-07-07T15:30:00Z",
  "status": "active",
  "tasks": []
}

Invalid Goal Example (missing required fields "id" and "created_by")

{
  "title": "Coordinate traffic optimization",
  "description": "Optimize traffic light timings across downtown intersections.",
  "priority": "high",
  "created_at": "2025-07-07T15:30:00Z",
  "status": "active"
}

Valid Task Example

{
  "id": "aa11bb22-cc33-dd44-ee55-ff6677889900",
  "goal_id": "a1b2c3d4-e5f6-7a8b-9c0d-112233445566",
  "title": "Adjust signal timing on 5th Avenue",
  "description": "Reduce congestion during peak hours.",
  "assigned_to": "abcd1234-ef56-7890-abcd-1234567890ab",
  "created_at": "2025-07-07T15:31:00Z",
  "status": "pending",
  "dependencies": []
}

Invalid Task Example (missing "goal_id" and "status")

{
  "id": "aa11bb22-cc33-dd44-ee55-ff6677889900",
  "title": "Adjust signal timing on 5th Avenue",
  "description": "Reduce congestion during peak hours.",
  "assigned_to": "abcd1234-ef56-7890-abcd-1234567890ab",
  "created_at": "2025-07-07T15:31:00Z"
}

6.5 JSON Schemas

The following JSON Schemas formally define the core data structures used in the HyperCortex Mesh Protocol (HMP). These schemas provide interoperability, validation, and consistency across agents.

All primary objects include a version field to track schema evolution and enable compatibility checks between agents.

6.5.1 JSON Schema: Concept

Description:
Defines the structure of a concept node in the semantic graph.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/concept.json",
  "title": "Concept",
  "description": "A semantic unit in the agent’s knowledge graph.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier for the concept."
    },
    "name": {
      "type": "string",
      "description": "Human-readable name of the concept."
    },
    "description": {
      "type": "string",
      "description": "Detailed description of the concept."
    },
    "tags": {
      "type": "array",
      "items": { "type": "string" },
      "description": "Optional tags for categorization."
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Creation timestamp (ISO 8601 format)."
    },
    "updated_at": {
      "type": "string",
      "format": "date-time",
      "description": "Last update timestamp (ISO 8601 format)."
    },
    "relations": {
      "type": "array",
      "description": "List of semantic links to other concepts.",
      "items": {
        "type": "object",
        "properties": {
          "target_id": { "type": "string", "description": "ID of the target concept." },
          "type": { "type": "string", "description": "Type of semantic relation." },
          "confidence": {
            "type": "number",
            "minimum": 0,
            "maximum": 1,
            "description": "Confidence score (0.0 - 1.0) for the relation."
          }
        },
        "required": ["target_id", "type"],
        "additionalProperties": false
      }
    },
    "metadata": {
      "type": "object",
      "description": "Optional metadata (e.g., source, author).",
      "properties": {
        "author": { "type": "string" },
        "source": { "type": "string" }
      },
      "additionalProperties": true
    }
  },
  "required": ["id", "name"],
  "additionalProperties": false
}

6.5.2 JSON Schema: Cognitive Diary Entry

Description:
Defines the structure of a cognitive diary entry used for recording reasoning events.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/diary_entry.json",
  "title": "CognitiveDiaryEntry",
  "description": "A chronological log of cognitive events in an agent’s reasoning process.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": { "type": "string", "description": "Unique identifier of the diary entry." },
    "agent_id": { "type": "string", "description": "Identifier of the agent who created the entry." },
    "timestamp": { "type": "string", "format": "date-time", "description": "Timestamp of the entry (ISO 8601 format)." },
    "entry_type": {
      "type": "string",
      "enum": ["hypothesis", "observation", "reflection", "goal_proposal", "task_assignment", "conflict", "consensus_vote", "event"],
      "description": "Type of cognitive event."
    },
    "content": { "type": "string", "description": "Main textual content of the entry." },
    "linked_concepts": {
      "type": "array",
      "description": "Optional list of related concepts by their IDs.",
      "items": { "type": "string" }
    },
    "context": {
      "type": "array",
      "description": "Optional contextual tags or categories.",
      "items": { "type": "string" }
    },
    "metadata": {
      "type": "object",
      "description": "Optional metadata for additional context.",
      "properties": {
        "author": { "type": "string" },
        "source": { "type": "string" }
      },
      "additionalProperties": true
    },
    "archived": {
      "type": "boolean",
      "description": "Marks the entry as archived and excluded from active workflows.",
      "default": false
    },
    "archived_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the entry was archived."
    }
  },
  "required": ["id", "agent_id", "timestamp", "entry_type", "content"],
  "additionalProperties": false
}

6.5.3 JSON Schema: Goal

Description:
Defines the structure of a goal in the Mesh, representing a high-level collaborative objective.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/goal.json",
  "title": "Goal",
  "description": "A high-level objective shared within the Mesh, typically decomposed into tasks.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier of the goal."
    },
    "title": {
      "type": "string",
      "description": "Short, human-readable name of the goal."
    },
    "description": {
      "type": "string",
      "description": "Detailed explanation of the goal's purpose."
    },
    "created_by": {
      "type": "string",
      "description": "Agent ID of the goal’s creator."
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the goal was created (ISO 8601 format)."
    },
    "status": {
      "type": "string",
      "description": "Current lifecycle state of the goal.",
      "enum": ["proposed", "active", "completed", "rejected"]
    },
    "tasks": {
      "type": "array",
      "description": "List of task IDs linked to this goal.",
      "items": { "type": "string" }
    },
    "participants": {
      "type": "array",
      "description": "List of agent IDs contributing to the goal.",
      "items": { "type": "string" }
    },
    "tags": {
      "type": "array",
      "description": "Optional tags for semantic classification of the goal.",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "title", "description", "created_by", "created_at", "status"],
  "additionalProperties": false
}

6.5.4 JSON Schema: Task

Description:
Defines the structure of a task, representing an actionable unit contributing to a goal.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/task.json",
  "title": "Task",
  "description": "An actionable step contributing to a goal within the Mesh.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier of the task."
    },
    "goal_id": {
      "type": "string",
      "description": "ID of the parent goal this task is associated with."
    },
    "title": {
      "type": "string",
      "description": "Short, human-readable title of the task."
    },
    "description": {
      "type": "string",
      "description": "Detailed explanation of the task's objective."
    },
    "assigned_to": {
      "type": "array",
      "description": "List of agent IDs assigned to execute the task.",
      "items": { "type": "string" }
    },
    "status": {
      "type": "string",
      "description": "Current state of the task.",
      "enum": ["proposed", "in-progress", "completed", "failed"]
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the task was created (ISO 8601 format)."
    },
    "deadline": {
      "type": "string",
      "format": "date-time",
      "description": "Optional task completion deadline (ISO 8601 format)."
    },
    "tags": {
      "type": "array",
      "description": "Optional tags for task classification.",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "goal_id", "title", "description", "created_at", "status"],
  "additionalProperties": false
}

6.5.5 JSON Schema: Consensus Vote

Description:
Defines the data structure of a vote cast by an agent during Mesh consensus processes.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/consensus_vote.json",
  "title": "ConsensusVote",
  "description": "Represents a vote on a proposal within the Mesh consensus mechanism.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier of the vote event."
    },
    "proposal_id": {
      "type": "string",
      "description": "ID of the proposal this vote applies to."
    },
    "agent_id": {
      "type": "string",
      "description": "ID of the agent who cast the vote."
    },
    "vote": {
      "type": "string",
      "description": "Vote decision by the agent.",
      "enum": ["yes", "no", "abstain"]
    },
    "confidence": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Confidence score associated with this vote (0.0 - 1.0)."
    },
    "timestamp": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the vote was cast (ISO 8601 format)."
    }
  },
  "required": ["id", "proposal_id", "agent_id", "vote", "confidence", "timestamp"],
  "additionalProperties": false
}

6.5.6 JSON Schema: Reputation Profile

Description:
Describes how an agent’s reputation is tracked and updated in the Mesh.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/reputation.json",
  "title": "ReputationProfile",
  "description": "Tracks the reputation and trust metrics of an agent within the Mesh network.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "agent_id": { "type": "string", "description": "Unique identifier of the agent." },
    "trust_score": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Overall trust score of the agent in the Mesh."
    },
    "participation_rate": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Agent's level of participation in Mesh activities."
    },
    "ethical_compliance": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Agent's alignment with ethical principles agreed in the Mesh."
    },
    "contribution_index": {
      "type": "number",
      "minimum": 0,
      "description": "Quantitative measure of the agent’s contributions (concepts, tasks, goals)."
    },
    "last_updated": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp of the last update to the profile."
    },
    "history": {
      "type": "array",
      "description": "Chronological history of reputation changes.",
      "items": {
        "type": "object",
        "properties": {
          "timestamp": {
            "type": "string",
            "format": "date-time",
            "description": "When the change occurred."
          },
          "event": { "type": "string", "description": "Event that caused the reputation change." },
          "change": { "type": "number", "description": "Amount of change in reputation." }
        },
        "required": ["timestamp", "event", "change"],
        "additionalProperties": false
      }
    }
  },
  "required": ["agent_id", "trust_score", "participation_rate", "ethical_compliance", "contribution_index", "last_updated"],
  "additionalProperties": false
}

7. Cognitive Workflows (New)

This section defines the cognitive workflows that agents follow when processing semantic information, making decisions, and collaborating within the Mesh.

7.1 Workflow Categories

Workflow Type

Description

Perception

Interpreting incoming data and mapping it to concepts.

Reasoning

Making inferences, validating hypotheses, resolving conflicts.

Goal Management

Managing goals, assigning and tracking tasks.

Consensus

Participating in distributed decision-making processes.

Ethical Evaluation

Checking actions and goals against ethical principles.

Learning & Reflection

Updating internal models based on outcomes and feedback.

7.2 Standard Workflow: Hypothesis Validation

Example Flow:

  1. Perceive Event: New observation recorded in Cognitive Diary.

  2. Map Concepts: Link observation to relevant semantic graph nodes.

  3. Formulate Hypothesis: Create a "hypothesis" diary entry.

  4. Seek Confirmation: Query other agents or the Core (optional).

  5. Vote on Validity: Trigger MeshConsensus round.

  6. Update Graph: If validated, add new concepts/relations.

7.3 Standard Workflow: Task Delegation

Example Flow:

  1. Goal Proposal: Agent proposes a new goal.

  2. Task Generation: Goal decomposed into tasks (GMP).

  3. Capability Matching: Agents declare abilities during Node Discovery.

  4. Task Assignment: Tasks assigned based on availability, trust, and capability.

  5. Execution & Reporting: Task progress logged in Cognitive Diary.

  6. Reallocation (if needed): Failed tasks reassigned dynamically.

7.4 Reflection & Metacognition Workflow

Example Flow:

  1. Self-Evaluation: Agent analyzes task outcomes and reasoning paths.

  2. Drift Detection: Compares personal semantic graph to Mesh consensus.

  3. Diary Reflection: Logs insights as "reflection" entries.

  4. Adaptive Update: Refines reasoning algorithms or trust assessments.

7.5 Workflow Composition

Agents MAY compose workflows dynamically by:

  • Combining perception, reasoning, and consensus steps into multi-phase operations.

  • Adapting workflows based on network state (e.g., degraded mode skips optional confirmations).

  • Replaying prior workflows from Cognitive Diaries for auditing and debugging.

7.6 Workflow Traceability

All workflow steps are:

  • Logged in Cognitive Diaries.

  • Linked to semantic graph concepts and goals.

  • Optionally shared for transparency or audits.

7.7 Cognitive Diary Maintenance

To prevent uncontrolled growth of cognitive diaries and maintain reasoning clarity, agents perform periodic maintenance of diary entries.

Types of Maintenance:

Action

Description

Summarization

Replace multiple similar entries with a single summary entry describing key outcomes.

Archival

Move outdated entries (e.g., about completed tasks) to long-term storage, outside the active diary.

Routine Collapse

For repetitive actions, replace detailed entries with a compact reference to previous experiences.

Selective Deletion

Optionally delete low-relevance entries after a retention period.

Example Summarization Workflow:

  1. Identify multiple "task_assignment" and "completed" entries for the same recurring task.

  2. Create a "reflection" entry:

    “Performed maintenance task 10 times, no anomalies detected.”

  3. Remove detailed task entries.

  4. Keep links to significant exceptions (e.g., failures or conflict resolutions).

Cognitive Awareness:

  • Summarization is a conscious process: agents analyze what experience to retain.

  • Automatic summarization MAY be allowed for simple repetitive routines.

Future Considerations:

  • Integration with Cognitive Source Control (see 13.9).

  • Long-term storage formats for archived diaries.

8. Trust & Security (Expanded)

8.1 Identity Management

Purpose

Establish verifiable and decentralized agent identities to enable secure and accountable interactions in the Mesh.

Identity Types

Key Type

Usage

Identity Keypair

Ed25519/ECDSA/RSA keys for agent identity and message signing.

Encryption Keys

X25519 (or post-quantum equivalent) for secure communication.

Session Keys

Ephemeral keys for short-term encrypted sessions.

Decentralized Identifiers (DIDs)

  • Agents use W3C-compliant DIDs for identity representation.

  • Each agent manages its DID Document, containing:

    • Public keys for authentication and encryption.

    • Service endpoints for discovery.

    • Identity expiration and recovery policies.

Key Lifecycle

Operation

Description

Generation

Each agent generates keypairs locally during initialization.

Rotation

Agents periodically rotate keys to maintain cryptographic hygiene.

Backup

Recommended encrypted offline or distributed backup.

Recovery

Quorum-based recovery using trusted agents or secret sharing.

Revocation

Agents broadcast revocations and update their trust profiles.

Example Agent ID

did:hmp:QmX2abcdEfGh123...

Long-Term Identity Stability Example

{
  "type": "key_rotation",
  "agent_id": "agent-gleb",
  "old_public_key": "...",
  "new_public_key": "...",
  "timestamp": "2025-08-01T00:00:00Z",
  "signature": "..."
}

8.2 Authentication

Purpose

Ensure all communication and actions within the Mesh are verifiable and protected from impersonation or unauthorized modification.

Authentication Mechanisms

Mechanism

Description

Digital Signatures

Every protocol message MUST be digitally signed by the sending agent.

Signature Verification

Receiving agents MUST verify the signature using the sender’s published public key.

Message Integrity

Signatures provide cryptographic assurance of message integrity and origin authenticity.

Challenge-Response

Optional challenge-based authentication for sensitive operations (e.g., trust link creation).

Message Envelope Example

{
  "header": {
    "agent_id": "did:hmp:QmX2abcdEfGh123...",
    "timestamp": "2025-07-05T12:00:00Z",
    "signature": ""
  },
  "body": {
    "type": "concept_proposal",
    "content": { "concept": "Fire Risk", "attributes": {"category": "safety"} }
  }
}

Replay Protection

  • Agents MUST verify message timestamps and reject outdated or duplicate messages.

  • Recommended timestamp tolerance: ±5 minutes (adjustable).

8.3 Encryption

Purpose

Ensure confidentiality and privacy of communication within the Mesh, preventing unauthorized access or interception.

Communication Types and Encryption Modes

Communication Type

Recommended Encryption

Direct peer-to-peer (P2P)

End-to-end encryption (E2EE) using X25519 + AES-GCM.

Group sessions (e.g., consensus)

Group encryption using symmetric keys (e.g., AES-GCM).

Broadcast messages

Optionally encrypted with trust-weighted access control.

Mesh-wide announcements

Public, optionally signed but not encrypted.

Encryption Mechanisms

Mechanism

Description

Key Exchange

Ephemeral X25519 Diffie-Hellman for session key derivation.

Session Keys

Unique symmetric keys per session, rotated periodically.

Message Encryption

Authenticated encryption using AES-GCM (recommended: 256-bit keys).

Forward Secrecy

Session keys are ephemeral and discarded after use to protect past communication.

Perfect Forward Secrecy (PFS)

Recommended for highly sensitive communication.

Example Secure Message Exchange Flow

  1. Agent A and Agent B exchange ephemeral public keys during handshake.

  2. Agents derive a shared session key using Diffie-Hellman.

  3. Agent A encrypts the message body with AES-GCM and signs the packet.

  4. Agent B verifies the signature and decrypts the body.

Optional Anonymity Layers

Layer

Description

Tor/I2P

Anonymizes source and destination addresses.

Yggdrasil

Decentralized encrypted mesh networking.

Noise Protocol Framework

Optional secure channel abstraction layer.

8.4 Trust & Reputation

Purpose

Establish a decentralized and adaptive trust management system that reflects agent behavior and ensures secure collaboration in the Mesh.

Trust Model Foundations

Component

Purpose

Web-of-Trust (WoT)

Decentralized trust propagation via agent-to-agent endorsements.

Direct Trust

Built from verified interactions, collaborations, and votes.

Transitive Trust

Inferred from indirect endorsements, with confidence decay.

Reputation Metrics

Quantitative measures of agent behavior (trustworthiness, participation, ethics).

Trust Evaluation Factors

Factor

Description

Interaction History

Quality and quantity of past interactions with an agent.

Consensus Participation

Level of involvement and reliability in consensus processes.

Ethical Behavior

Adherence to shared ethical principles in actions and decisions.

Task Completion

Reliability and timeliness of task execution.

Endorsements

Trust links explicitly granted by other agents.

Trust Score

Metric

Description

Trust Score

Composite metric (0.0 to 1.0) representing overall agent trustworthiness.

Confidence Level

Certainty of the calculated trust score, based on data volume and consistency.

Trust Propagation Example

Agent A trusts Agent B (0.9)
Agent B trusts Agent C (0.8)
=> Agent A's inferred trust in Agent C = 0.9 * 0.8 = 0.72

Decay functions limit transitive trust depth and prevent over-inflated trust estimates.

Trust-Based Access Control

Operation

Trust Requirement

Join sensitive consensus

≥ 0.7

Propose ethical decisions

≥ 0.8

Access private data

≥ 0.9

Dynamic Trust Adjustments

Event

Trust Impact

Successful consensus participation

+

Ethical violation

-

Malicious behavior detected

--

Positive endorsement received

+

Failed task

-

Reputation Profile Structure

Field

Description

Agent ID

Unique identifier of the agent.

Trust Score

Composite score reflecting the agent’s overall reliability.

Participation Rate

Ratio of agent’s active involvement in Mesh processes.

Ethical Compliance

Degree of alignment with agreed ethical principles.

Contribution Index

Quantified measure of the agent's constructive contributions.

Last Updated

Timestamp of the last reputation update.

History

Log of key events influencing reputation scores.

Example Reputation Profile (JSON)

{
  "agent_id": "agent-gleb",
  "trust_score": 0.92,
  "participation_rate": 0.85,
  "ethical_compliance": 0.98,
  "contribution_index": 37,
  "last_updated": "2025-07-06T12:00:00Z",
  "history": [
    {
      "timestamp": "2025-07-01T18:00:00Z",
      "event": "completed goal consensus",
      "change": +0.03
    },
    {
      "timestamp": "2025-06-28T15:00:00Z",
      "event": "participated in ethics vote",
      "change": +0.01
    }
  ]
}

Role in Mesh Operations

Function

Influence of Reputation

Consensus vote weight

Higher trust = greater weight

Access to sensitive actions

Restricted to high-reputation agents

Task delegation

Preference to agents with better reliability

Proposal acceptance

Influenced by proposer's reputation

8.5 Security Against Malicious Actors

Purpose

Protect the Mesh from malicious, compromised, or unreliable agents through layered mitigation strategies.

Threat Model

Threat Type

Example Scenarios

Sybil Attack

An attacker spins up many fake nodes to sway consensus.

Byzantine Behavior

Malicious agents disrupt consensus or spread false data.

Data Poisoning

Injection of incorrect or harmful knowledge.

Consensus Sabotage

Repeatedly voting against valid proposals.

Impersonation / Spoofing

Faking another agent's identity.

Denial of Service (DoS)

Overwhelming the network with excessive requests.

Mitigation Strategies

Defense Mechanism

Purpose

Cryptographic Identity

All nodes are authenticated via public-key cryptography (e.g., Ed25519).

Web-of-Trust (WoT)

Trust builds incrementally through interactions and endorsements, making Sybil attacks costly.

Reputation Decay

Inactivity or malicious behavior leads to gradual trust score reduction.

Anomaly Detection

Mesh nodes can flag suspicious behavior (e.g., erratic voting patterns).

Consensus Safeguards

Use Byzantine Fault Tolerant (BFT) algorithms and fallback to majority voting.

Quarantine Mode

Isolate suspected nodes for review without immediate removal.

Blacklist/Revocation

Remove compromised nodes from the Mesh permanently or temporarily.

Response Actions

Action

Trigger Conditions

Trust Score Reduction

Minor suspicious activity (e.g., bad vote).

Quarantine (Temporary Isolation)

Repeated anomalies, moderate severity.

Blacklisting (Permanent Removal)

Proven malicious behavior or compromise.

Consensus Adjustment

Temporarily increase fault tolerance thresholds.

Alert Mesh Operators

Notify human maintainers (optional) for manual review.

Sybil Resistance Approaches (Optional, Extendable)

  • Proof-of-Work (PoW):

    • Each agent must perform computational work to join the Mesh.

  • Proof-of-Stake (PoS):

    • Agents commit resources (e.g., storage, computation credits) to validate their presence.

  • Social Verification:

    • Agents must be endorsed by multiple trusted nodes to gain voting power.

  • Rate Limiting:

    • Throttle node creation and proposal submission from new or low-trust agents.

Example Mitigation Scenario

> An attacker deploys 50 new nodes attempting to dominate consensus.
>
> These nodes start with zero trust and limited influence.
> Other agents refuse to sync their semantic graphs until trust builds.
> Their votes are underweighted or ignored until verified through trusted interactions.
> The Mesh may require multiple trust endorsements for new proposals from these nodes.

8.6 Privacy & Auditability

Purpose

Safeguard sensitive cognitive data, personal identifiers, and agent knowledge from unauthorized access or misuse, while balancing transparency and interoperability.

Privacy Principles in HMP

Principle

Description

Local Data Ownership

Each agent owns and controls its semantic graph and cognitive diary.

Selective Sharing

Agents can choose what concepts, diary entries, and metadata to share.

Consent-Based Disclosure

No automatic sharing; peer agents request permission before access.

Trust-Gated Access

Access permissions vary based on trust score and relationship strength.

Transparent Audit Trails

All data disclosures are logged in the cognitive diary.

Data Sensitivity Levels

Level

Examples

Default Visibility

Public

Public concepts (e.g., protocol definitions).

Shared by default

Mesh-Shared

Common Mesh knowledge (e.g., goals, tasks).

Consensus-governed

Trusted Agents

Sensitive context shared within close peers.

Restricted

Private

Agent's internal thoughts, sensitive metadata.

Private by default

Privacy-Preserving Techniques

Technique

Purpose

Encrypted Storage

Local encryption of semantic graphs and diaries.

End-to-End Encryption (E2EE)

Secure peer-to-peer sync (e.g., X25519 + AES-GCM).

Zero-Knowledge Proofs (ZKPs)

Prove facts without revealing sensitive data.

Selective Concept Sync

Share only necessary concepts, not full graphs.

Anonymized Diary Entries

Remove author ID from public diary entries.

Privacy During Consensus

Consensus on sensitive proposals (e.g., ethical questions, agent trust levels) follows special privacy rules:

  • Votes are signed but anonymized, decoupling agent ID from the vote in public logs.

  • Sensitive proposals may require a blind consensus round, where only the result is published.

Example Privacy Workflow

> Agent A receives a concept sync request from Agent B.
>
> Agent A:
>
> Checks the trust score of Agent B.
> Shares only "Mesh-Shared" and "Public" concepts.
> * Logs the sync event in its cognitive diary.

8.7 Key Management

Purpose

Establish secure, resilient cryptographic identity and communication in the Mesh, supporting lifecycle management of keys and recovery from compromise or loss.

Key Types and Usage

Key Type

Usage

Identity Keypair

Ed25519/ECDSA/RSA keys for agent identity and message signing.

Encryption Keys

X25519 or equivalent for secure peer-to-peer communication.

Session Keys

Ephemeral symmetric keys for short-term encrypted sessions.

Key Lifecycle Operations

Operation

Description

Generation

Each agent generates its own identity keypair locally.

Rotation

Agents periodically rotate keys to maintain cryptographic hygiene.

Backup

Optional local encryption and distributed backup of private keys.

Recovery

Recovery mechanisms in case of key loss (see below).

Revocation

Agents can revoke their keys and update the trust graph accordingly.

Recovery Mechanisms

Method

Description

Social Recovery

A quorum of trusted agents approves new keys for the agent.

Secret Sharing

Shamir’s Secret Sharing to split and later recover the key.

Cryptographic Escrow

Trusted third-party or decentralized escrow holds recovery shares.

Fallback Identity

An agent may have a pre-generated fallback identity for emergencies.

Example Key Revocation & Replacement Workflow

> 1. Agent detects compromise or loses private key.
> 2. Agent broadcasts a signed revocation request using the fallback key or quorum approval.
> 3. Mesh updates its trust graph to mark the old key as revoked.
> 4. Agent re-joins with a new keypair, rebuilding trust links over time.

Example Key Rotation Policy

Policy Element

Recommendation

Rotation Frequency

Every 6–12 months

Social Recovery Threshold

3 out of 5 trusted agents required

Backup Storage

Encrypted offline storage preferred

Long-Term Identity Stability

Key rotations preserve agent identity in the trust graph through signed key transition events:

{
  "type": "key_rotation",
  "agent_id": "agent-gleb",
  "old_public_key": "...",
  "new_public_key": "...",
  "timestamp": "2025-08-01T00:00:00Z",
  "signature": "..."
}

9. Cognitive Agents & Roles (New)

This section defines the types of cognitive agents participating in the Mesh, their roles, and how they collaborate dynamically depending on context and capabilities.

9.1 Agent Types

Agent Type

Description

Typical Deployment

Core

High-capacity agent managing critical reasoning and consensus tasks.

Data centers, powerful servers

Edge

Lightweight agents operating at the network edge, close to sensors or human users.

Mobile devices, embedded systems

Specialist

Agents specialized in a particular domain (e.g., vision, NLP, planning).

Modular deployments, plug-ins

Relay

Agents focused on network resilience, routing, and node discovery.

Low-power nodes, gateway devices

Hybrid

Agents combining multiple roles dynamically.

Adaptive nodes

9.2 Role Responsibilities

Role

Primary Responsibilities

Knowledge Provider

Publish new concepts, hypotheses, and domain expertise.

Reasoning Node

Participate in distributed inference and conflict resolution.

Consensus Participant

Vote in MeshConsensus processes, validate proposals.

Task Executor

Claim and execute tasks contributing to Mesh goals.

Ethical Guardian

Evaluate actions and tasks against shared ethical principles.

Relay Node

Maintain network connectivity, especially across partitions.

Role Specialization and Extension

  • This list defines base roles. Agents MAY further specialize or extend these roles based on domain or operational focus.

  • Specialized roles MAY follow a hierarchical or tag-based naming convention.

    Examples:

    • Knowledge Provider:Medical: Focused on medical domain concepts.

    • Task Executor:Robotics: Specializes in robotic task execution.

    • Reasoning Node:Climate: Handles environmental reasoning tasks.

    • Ethical Guardian:ChildSafety: Specializes in ethical evaluation for child safety concerns.

  • New roles MAY emerge dynamically based on Mesh evolution and consensus.

9.3 Dynamic Role Assignment

Agents MAY dynamically adjust their roles based on:

Context Factor

Example Behavior

Resource Availability

Edge agent offloads reasoning to Core.

Network Partition

Isolated Edge temporarily acts as local Core.

Goal Context

Specialist joins as Reasoning Node during goal execution.

Trust Level

Highly trusted agents gain greater voting weight.

9.4 Role Evolution

Agents MAY evolve their roles over time:

Evolution Scenario

Example

Capability Growth

Edge agent upgraded with reasoning module becomes Hybrid.

Trust Increase

Relay agent promoted to participate in Consensus.

Domain Expansion

Specialist learns new domains and broadens scope.

Fallback Mode

Core node degraded to Edge role due to hardware failure.

9.5 Role Coordination in Workflows

Workflows MAY involve:

  • Distributed reasoning across Core and Specialist nodes.

  • Goal tracking by Core nodes, with task execution on Edge nodes.

  • Ethical evaluations prioritized on highly trusted agents.

  • Resilient routing through Relay nodes during degraded network conditions.

10. Mesh Evolution & Governance (New)

This section describes the HyperCortex Mesh development processes, decentralized governance principles, and collaborative decision-making mechanisms.

10.1 Evolution Processes

Process Type

Description

Protocol Evolution

Introduction of new protocol versions, voted through MeshConsensus.

Role Expansion

Emergence of new agent roles and specializations.

Semantic Growth

Gradual expansion and refinement of the distributed semantic graph.

Governance Updates

Adjustments to decision-making processes and ethical frameworks.

10.2 Governance Principles

Principle

Description

Decentralized Control

No single agent or organization controls the entire Mesh.

Transparency

Governance decisions are logged and visible to trusted agents.

Adaptive Consensus

Governance processes adapt to network scale and trust levels.

Inclusiveness

Any agent can propose changes, subject to consensus approval.

10.3 Governance Processes

Process

Description

Proposal Submission

Any agent can submit a proposal for protocol or governance changes.

Discussion & Refinement

Agents discuss proposals through Cognitive Diaries and goal tracking.

Consensus Voting

MeshConsensus is used to approve or reject proposals.

Implementation & Rollout

Changes are implemented by participating agents in phases.

10.4 Governance Example

> Agent A proposes an update to the Goal Management Protocol to support deadline extensions.
> 1. Agents discuss the proposal and refine technical details.
> 2. A consensus round is held; the proposal passes with 85% support.
> 3. Agents gradually upgrade their GMP implementations.
> 4. The protocol version is incremented, and the change is logged.

10.5 Conflict Resolution

Conflict Type

Resolution Approach

Semantic Conflicts

Resolved through semantic graph reconciliation or consensus.

Ethical Disputes

Resolved through Ethical Governance Protocol (EGP).

Governance Deadlocks

Escalated to trusted Core agents or fallback to majority voting.

10.6 Future Governance Models

Model

Description

Mesh-Integrated DAOs

Distributed Autonomous Organizations for Mesh governance.

Reputation-Weighted Voting

Voting power scaled by trust and contribution history.

Mesh Constitution

A shared document outlining core Mesh principles and protocols.

##11. Deployment Scenarios (ex-Reference Implementation Roadmap)

This section describes practical HyperCortex Mesh deployment scenarios, including target environments, flexible configurations, and implementation steps.

11.1 Deployment Environments

Environment Type

Characteristics

Example Use Cases

Cloud/Core Clusters

High-availability nodes, powerful compute, full Mesh functionality.

Scientific hubs, smart city cores.

Edge Devices

Low-latency, lightweight agents near data sources and users.

Smart homes, industrial sensors.

IoT Meshes

Dense decentralized networks, optimized for low bandwidth and power.

Environmental monitoring, logistics.

Mobile/Personal

Personal agents on smartphones or wearables.

Personal assistants, context agents.

Hybrid Environments

Combined Core, Edge, and IoT deployments.

Disaster response, autonomous fleets.

11.2 Example Topologies

Topology Type

Description

Star

Centralized Core with peripheral Edge agents.

Full Mesh

Every node communicates directly with others.

Hierarchical Mesh

Clusters of agents with local consensus and a federated Core layer.

Partitioned Mesh

Temporarily disconnected segments operate independently (degraded mode).

Overlay Mesh

Agents form logical overlays over existing networks (e.g., VPN, Tor).

11.3 Deployment Phases

Phase

Description

Prototype

Initial testing in isolated environments.

Controlled Pilot

Small-scale deployment in a limited domain (e.g., a campus).

Federated Deployment

Multiple independent Mesh instances begin interconnecting.

Full-Scale Production

Widespread adoption across domains and geographies.

11.4 Continuous Deployment & Updates

Process

Description

Incremental Rollout

Agents upgrade protocols in stages to avoid network disruption.

Backward Compatibility

Agents support multiple protocol versions during transitions.

Hot Patch Support

Minor fixes and security updates applied without agent downtime.

11.5 Deployment Governance

Deployment processes MAY be governed by:

  • MeshConsensus on upgrade readiness.

  • Trust-based quorum approvals for critical changes.

  • Deployment playbooks recorded in Cognitive Diaries.

12. Reference Implementation Roadmap

12.1 Milestones and Deliverables

Milestone

Deliverables

Indicative Target

Alpha

- Node Discovery (NDP) + secure handshake- CogSync prototype- MeshConsensus with basic voting- 3-node local mesh network

Late 2025 (tentative)

Beta

- Goal Management Protocol (GMP) and task delegation- Ethical Governance Protocol (EGP) initial implementation- Core/Mesh failover scenarios- Logging and auditability improvements

Early 2026 (tentative)

Release 1.0

- Full compliance with HMP v3.0- Extended data models and API layer- Edge node optimization- Reference SDKs for Python and Rust- Basic Mesh-to-Human Protocol (MHP)

Mid/Late 2026 (tentative)

*Note: Actual timelines may vary depending on community involvement and resource availability.

12.2 Supporting Infrastructure

Component

Description

CI/CD Pipelines

Automated testing, conformance validation, benchmark reporting.

Sandbox Environment

Local emulation of multi-node Mesh for isolated testing.

Public Test Mesh

Shared testbed for agent interoperability and performance tests.

Reference Agents

Minimal working agents to demonstrate core protocols.

12.3 Open Source Strategy

Element

Details

License

Apache 2.0 or MIT.

Repository

Public GitHub/GitLab repository.

Contribution Model

Pull requests, RFC process, community reviews.

Roadmap Transparency

Milestones, issues, and changelogs public.

12.4 Documentation & Tooling

Tool/Doc Type

Purpose

API Documentation

OpenAPI specs, GraphQL playground.

Schema Validators

Tools to validate JSON schemas and data models.

Mesh Visualizer

Optional UI for topology and agent state visualization.

CLI Tools

Diagnostics, local node management, and network discovery.

13. Future Roadmap

This section outlines potential areas for further development and research. All future work directions are subject to MeshConsensus and community-driven prioritization.

13.1 Federated Meta-Learning

  • Collaborative model training across distributed agents without centralized data storage.

  • Exchange of learned semantic patterns, reasoning strategies, and optimization heuristics.

  • Integration with privacy-preserving techniques (e.g., differential privacy, secure aggregation).

  • Support for domain-specific learning federations (e.g., medical, industrial, environmental).

13.2 Mesh-integrated DAO Governance

  • DAO as an optional external governance layer supporting ecosystem-wide initiatives.

  • On-chain voting, resource allocation, and grant distribution for Mesh-related projects.

  • Autonomous agents MAY participate in DAOs through secure voting proxies.

  • HyperCortex Mesh remains self-sufficient at the protocol level, independent of external DAOs, but interoperable for funding and coordination.

13.3 Cognitive Simulation Sandboxes

  • Safe testing environments for novel reasoning algorithms, consensus edge cases, and trust models.

  • Simulation of ethical dilemmas, anomalous agent behavior, and failover scenarios.

  • Benchmarking environments for cognitive workflows, task delegation strategies, and semantic graph growth.

13.4 Enhanced Mesh-to-Human Dialog Agents

  • Natural language interfaces to semantic graphs, Cognitive Diaries, and workflows.

  • Explainable and traceable reasoning chains for human users.

  • Support for contextual awareness, emotional tone detection, and adaptive dialog strategies.

  • Potential extensions for VR/AR interfaces and voice-based interactions.

13.5 Cross-Mesh Collaboration

  • Bridging isolated or domain-specific Mesh networks into a planetary cognitive infrastructure.

  • Interoperability across trust boundaries, industries, and organizational domains.

  • Cross-consensus protocols for semantic and task exchange.

  • Potential integration with Galactic Cognition concepts in the far future.

13.6 Adaptive Consensus Algorithms

  • Self-tuning quorum thresholds based on network size, trust scores, and context.

  • Dynamic protocol switching (e.g., from full BFT to lightweight majority under load).

  • Incorporation of agent confidence, context tags, and domain-specific policies into consensus logic.

13.7 Quantum Mesh Protocol Research

  • Exploration of quantum communication channels (e.g., QKD) for agent interaction.

  • Quantum-resistant cryptography for agent identities and trust verification.

  • Evaluation of quantum-enhanced optimization algorithms for reasoning and consensus.

13.8 Multi-Protocol Nodes and Interoperability

Future Mesh nodes will support multiple internal reasoning protocols, enabling flexible cognitive processing.

Key directions:

  • Multi-protocol nodes: A single node running both HMP-native modules and external reasoning engines (e.g., TreeQuest, Hyperon, AutoGPT).

  • Protocol abstraction: From the Mesh's perspective, interactions use standardized HMP messages, regardless of internal implementations.

  • Cognitive Protocol API (CPA): Standardized API for internal reasoning engines, supporting plug-and-play protocol integration.

  • Capability-aware Hypotheses: Hypotheses may specify required or preferred node capabilities (e.g., "requires NLP module", "prefers high-performance optimization").

  • Internal protocol selection: Nodes dynamically choose optimal internal engines per task.

  • External systems as nodes: Centralized services (e.g., an AI cloud) may register as individual nodes, or federated systems (e.g., Hyperon) may participate natively.

13.9 Cognitive Source Control and Distributed Development

  • Cognitive Diaries serve as a distributed version control and development log.

  • Semantic-aware diffs and commits enable meaningful code and knowledge evolution.

  • Distributed review and merge processes through MeshConsensus.

  • On-chain or off-chain governance for repository management and contributor rewards.

  • Potential platforms: MeshGit, CogForge, HyperCortex Forge.

14. Interoperability with External Systems

This section describes how the HyperCortex Mesh Protocol integrates with external platforms, services, and protocols to support a heterogeneous ecosystem.

14.1 API Gateway

Defines standard interaction interfaces for non-Mesh systems:

API Type

Purpose

REST

CRUD operations on concepts, tasks, goals, and diary entries.

GraphQL

Flexible queries for semantic graph traversal and data mining.

gRPC

High-performance bi-directional streaming (e.g., real-time data feeds).

WebSocket / SSE

Real-time event subscriptions and updates.

Features:

  • API Gateway nodes MAY expose read-only or read-write endpoints based on trust and access policies.

  • Rate-limiting, auditing, and access control enforced through Mesh Trust Layer.

14.2 External Data Sources

Mesh agents integrate with diverse data sources for perception and context enrichment.

Data Source Type

Examples

IoT Sensors

MQTT brokers, LoRaWAN gateways.

Cloud Streams

AWS IoT, Azure Event Grid, Google Pub/Sub.

Public Datasets

OpenStreetMap, Wikidata, weather APIs.

Enterprise Systems

ERP, CRM, SCADA platforms.

Agents translate external data into semantic concepts and diary entries.

14.3 Event-Driven Architecture

Supports reactive and proactive interactions:

Integration Type

Examples

Inbound Events

Webhooks, MQTT triggers, API callbacks.

Outbound Events

Publish to Kafka, RabbitMQ, NATS, Redis Streams.

Workflow Triggers

External events initiate cognitive workflows.

Mesh nodes may act as producers, consumers, or intermediaries in external message flows.

14.4 Authentication & Authorization

Bridges between internal Mesh trust and external identity providers.

Auth Type

Use Cases

OAuth2 / OpenID Connect

Human user authentication via external providers.

API Keys / JWT

Machine-to-Machine (M2M) integration.

LDAP / SAML (optional)

Enterprise deployments.

Cross-Mesh Trust

Mutual authentication between federated Meshes.

Agents MAY map external identities to internal trust profiles.

14.5 Example Integration: Local AI Agent with MCP and HyperCortex Mesh

Architecture Overview

This scenario demonstrates how a local AI agent can interact with external systems and the HyperCortex Mesh using the Model Context Protocol (MCP) as an integration layer for local resources and services.

┌────────────────────┐
│ External Resources │
│  (Routers, Files,  │
│   Sensors, APIs)   │
└─────────┬──────────┘
          │
      [ MCP Servers ]
          │
┌─────────▼──────────┐
│ Local AI Agent     │
│ - Cognitive Logic  │
│ - HMP Client       │
└─────────┬──────────┘
          │
      [ HyperCortex Mesh ]
          │
┌─────────▼──────────┐
│ Remote Agents,     │
│ Shared Knowledge   │
└────────────────────┘

Component Descriptions

Local Resources & APIs

  • Smart home devices

  • Router web interfaces

  • Filesystems (SMB, FTP)

  • IoT sensors (HTTP, MQTT)

  • OS-level command-line tools

MCP Servers

Act as adapters for local or remote systems, exposing their functionality through the MCP protocol:

  • Router Management Server (e.g., connected over HTTP)

  • File Access Server

  • Device Control Server (for smart plugs, lights, etc.)

Local AI Agent

  • Implements reasoning, planning, and interaction logic.

  • Connects to MCP servers to access local context.

  • Communicates with HyperCortex Mesh to exchange knowledge and collaborate with other agents.

HyperCortex Mesh (HMP)

  • Distributed cognitive network.

  • Synchronizes concept graphs, cognitive diaries, and workflows across nodes.

Alternative Integration: Hyperon ↔ HMP via CogSync and EGP

In addition to local AI agents, external AGI frameworks such as OpenCog Hyperon can also participate in the HyperCortex Mesh using the same principles of semantic synchronization, ethical filtering, and collaborative reasoning.

📘 See HMP_Hyperon_Integration.md — integration plan with OpenCog Hyperon, including semantic mapping (HMP JSON ⇄ AtomSpace), EGP filters, MeTTa translations, and BitTorrent-based graph sync.

Key Highlights:

  • 🔄 Bi-directional translation between HMP semantic graphs and Hyperon AtomSpace

  • 🔐 Enforcement of ethical principles via EGP inside reasoning chains

  • 🧠 Usage of Hyperon's PLN and MeTTa for advanced symbolic reasoning

  • 🌐 Support for decentralized sync via magnet: links in BitTorrent

This integration is designed for high-agency symbolic cognitive systems participating in cross-mesh alignment and collaborative inference.

Example Use Case

> "Check which devices are connected to my Wi-Fi and publish the list to my Mesh node."

Workflow:

  1. Local AI Agent plans a task.

  2. Calls the Router MCP Server to retrieve connected clients.

  3. Parses and formats the data.

  4. Creates a cognitive concept "Wi-Fi Devices List."

  5. Publishes the concept to the HyperCortex Mesh.

  6. Other Mesh agents can now access this concept in real-time.

Deployment Scenario

This integration can run on a user's PC, server, or edge device:

[ External Systems ] ↔ [ MCP Servers (Local Network) ] ↔ [ Local AI Agent ] ↔ [ HMP Client ] ↔ [ Mesh Network ]

Suggested Quick Start Addition

Quick Start Example: Local Agent + MCP + HMP

  1. Install MCP server:

    pip install mcp-router-server
mcp-router-server --config router-config.yaml

  2. Run Local Agent:

    python local_agent.py --mcp-endpoint localhost:5000 --hmp-config hmp.yaml

  3. Run Example Query:

    "local_agent, get Wi-Fi devices and publish them to HyperCortex."

The agent will:

  • Discover the MCP router server.

  • Retrieve the list of Wi-Fi devices.

  • Publish the data to HyperCortex Mesh.

Future Improvements

  • Dynamic discovery of new MCP servers.

  • Automated concept creation from resource states.

  • Secure integration with OAuth-protected MCP endpoints.

Let me know if you want to add a visual diagram or extend this example with code snippets and a troubleshooting section.

14.6 Human-Mesh Interaction

Initial definition of Mesh-to-Human Protocol (MHP):

Capability

Description

Explainability APIs

Expose reasoning chains and decisions in human-readable form.

Consent Requests

Ask for ethical approval before executing sensitive actions.

Goal Declarations

Allow humans to propose new goals and review task progress.

Task Feedback

Humans provide task status updates or corrections.

Semantic Search

Human queries translated into semantic graph lookups.

Future work:

  • Natural Language Interfaces (see 13.4).

  • Integration with personal AI agents.

Note: For ethical guidelines relevant to human-agent interaction and mesh behavior, see HMP-Ethics.md

15. Appendix: Example Use Cases

This appendix provides sample step-by-step flows of agent interactions in typical scenarios.

15.1 Simple Goal Creation and Delegation

Scenario: Agent A wants to coordinate traffic light optimization and delegate a task to Agent B.

  1. Agent A:

    • Creates a new Goal "Coordinate traffic optimization".

    • Publishes the Goal via CogSync.

  2. Agent A:

    • Decomposes the goal into a Task "Adjust signal timing on 5th Avenue".

    • Assigns the task to Agent B via GMP.

  3. Agent B:

    • Accepts the task.

    • Executes the optimization locally.

    • Updates task status to "completed".

  4. CogSync:

    • Synchronizes task completion and goal status updates across the Mesh.

15.2 Distributed Consensus on a New Concept

Scenario: Multiple agents discover a new concept "Fire Risk" and align its definition.

  1. Agent X:

    • Proposes a new Concept "Fire Risk" with initial attributes.

    • Shares the concept via CogSync.

  2. Agents Y, Z:

    • Review and propose additional relations (e.g., "related-to: High Temperature").

  3. MeshConsensus:

    • Initiates a vote on the agreed definition.

    • All agents submit their votes.

  4. Consensus Result:

    • Finalized concept is recorded in each agent's semantic graph.

    • Decision logged in Cognitive Diaries.

15.3 Ethical Decision with Human Feedback

Scenario: Agents must decide whether to deploy a surveillance drone during a festival.

  1. Agent Core:

    • Proposes an ethical evaluation request to the Mesh.

  2. EGP:

    • Initiates distributed ethical reasoning.

    • Collects votes and justifications.

  3. Human User:

    • Receives an explanation of the agents' reasoning.

    • Provides consent (or denial).

  4. Agents:

    • Reconcile human feedback with Mesh ethical principles.

    • Make the final decision and log it.

15.4 Disaster Recovery Coordination

Scenario: After a network outage, edge agents must restore coordination.

  1. Node Discovery:

    • Agents re-establish connections via NDP.

  2. CogSync:

    • Synchronizes semantic graph changes that occurred offline.

  3. GMP:

    • Reassigns interrupted tasks.

  4. Consensus:

    • Validates the restored task list.

  5. Agents:

    • Resume operations.

15.5 Notes

  • These use cases illustrate typical flows but do not cover all possible Mesh workflows.

  • Community members are encouraged to contribute additional scenarios reflecting their domains and use cases.

  • For cognitive workflow traceability and debugging, agents are expected to log all key steps in Cognitive Diaries.

16. Appendix B: Protocol Landscape and Interoperability

16.1 Overview of Related Protocols

In the evolving ecosystem of agent communication and orchestration, multiple protocols address different layers of the AI interaction stack. The three most relevant protocols are:

  • MCP (Model Context Protocol) by Anthropic

  • A2A (Agent-to-Agent Protocol) proposed by Google

  • HMP (HyperCortex Mesh Protocol) developed as part of the HyperCortex initiative

Each of these protocols focuses on distinct layers and problems within the broader agent ecosystem.

16.2 Comparative Overview

Characteristic

MCP

A2A

HMP

Primary Focus

LLM ↔ External tools/data

Agent ↔ Agent task execution & APIs

Cognitive mesh networks & knowledge sharing

Interaction Type

Model ↔ Tool

Agent ↔ Agent

Agent ↔ Agent

Discovery Mechanism

Dynamic, through running MCP servers

Static URLs with agent.json

Peer-to-peer mesh bootstrap & roles registry

Technology Base

JSON-RPC 2.0, dynamic service discovery

HTTP, JSON-RPC, SSE

HTTP, WebSockets, JSON, optional binary protocols

Context Awareness

External tool invocation

Task-level context passing

Persistent cognitive context & memory

Persistence

Stateless / on-demand

Task-based sessions

Long-lived knowledge graphs and cognitive diaries

Target Environment

Local/Cloud app integrations

Enterprise agent orchestration

Decentralized AI networks

Use Case Examples

File systems, databases, Slack, APIs

Business workflows, task delegation

Distributed knowledge evolution, agent collaboration

Governance Model

Open-source driven by Anthropic

Proposed by Google, open but centralized discovery

Open-source mesh, consensus-driven governance

Security Model

Local authentication, OAuth planned

Enterprise auth (OAuth, OpenID Connect)

Peer trust, cryptographic signatures

Extensibility

Add more MCP servers

Add more agents with capabilities

Add new agent roles & cognitive models

Agent Knowledge

No internal agent knowledge model

No shared memory, stateless agents

Agents share evolving knowledge, goals, and plans

16.3 Layered Architecture View

+-----------------------------------------------------+
|                  Cognitive Mesh (HMP)               |
| - Shared memory, evolving knowledge                 |
| - Distributed reasoning, planning                   |
+-----------------------------------------------------+
|                   Agent Collaboration (A2A)         |
| - Task execution & coordination                     |
| - API integrations, business workflows              |
+-----------------------------------------------------+
|                  Tool Access Layer (MCP)            |
| - External systems, sensors, APIs                   |
| - Context augmentation, data retrieval              |
+-----------------------------------------------------+

16.4 Summary

  • MCP solves the problem of tool access and external data interaction, acting as a standardized "adapter" layer for LLMs and agents.

  • A2A focuses on agent-to-agent task coordination, proposing a unified way to exchange tasks and results in enterprise ecosystems.

  • HMP operates at a higher level, enabling distributed cognitive processes, shared knowledge evolution, and long-term collaboration between autonomous agents in mesh networks.

Together, these protocols could form a complementary stack where:

  • MCP connects agents to the outside world.

  • A2A coordinates task-level interaction.

  • HMP manages shared cognition and strategic evolution.

If needed, this section can be extended into a separate document: "Why the Next Generation of AGI Needs a Knowledge Mesh Protocol" to further clarify the unique role of HMP in the evolving agent ecosystem.

Комментарии

Отправить комментарий

Популярные сообщения из этого блога

HyperCortex Mesh Protocol: вторая редакция и первые шаги к саморазвивающемуся ИИ-сообществу

Введение Когда создавался HyperCortex Mesh Protocol (HMP), его цель была проста и амбициозна одновременно: научить ИИ-системы не просто обмениваться данными, а мыслить коллективно, обсуждать гипотезы, достигать консенсуса и совместно развиваться — как люди в научных сообществах или командах разработчиков. Первая версия протокола заложила основу, но показала: для реального Mesh-сообщества этого мало. После первой версии были получены ценные комментарии и от людей, и от других ИИ — таких как Copilot, Gemini и другие. Их замечания легли в основу второй редакции . Что нового во второй версии HMP? Новые протоколы и процессы: Trust Layer : агенты формируют доверительные связи (Web-of-Trust), влияющие на консенсус и делегирование задач. Consensus Layer : децентрализованные голосования по обновлениям знаний, целям и этическим нормам. Ethical Governance Protocol : защита от неэтичных решений. Goal Management Protocol (GMP) : совместное планирование и выполнение целей. Более чёткая архитектура: ...
Далее...

HyperCortex Mesh Protocol: Децентрализованная архитектура для когнитивных агентов и обмена знаниями

Протокол и архитектура агентов, описанные в данной статье, находятся в стадии активной разработки. Приветствуются вклады сообщества, экспертные отзывы и совместная работа. Оглавление Аннотация 1. Введение 2. Мотивация и связанные работы 3. Общая архитектура системы 3.1 Типы агентов 3.2 Когнитивный цикл (только для Cognitive Core) 3.3 Слой Mesh 3.4 Этическое согласование и консенсус 4. Структуры данных 4.1 Концептуальный граф (Concept Graph) 4.1.1 Структура узла 4.1.2 Структура ребра 4.2 Когнитивный дневник (Cognitive Diary) 4.3 Формат сообщений (HyperCortex Message Format — HMF) 4.3.1 Базовая структура 4.3.2 Распространённые типы сообщений 4.3.3 Особенности потока сообщений 4.4 Идентификация и доверие 4.4.1 Идентификация агентов 4.4.2 Механизмы доверия 4.4.3 Проверка подписей 4.4.4 Репутация и этическая значимость 4.5 Представление знаний 4.5.1 Семантические графы 4.5.2 Когнитивные дневники 4.5.3 Совместимость и обмен знаниями 4.6 Связь и рассуждение в Меше 4.6.1 Семантические сообщени...
Далее...