HyperCortex Mesh Protocol v2.0 (HMP-0002)

HyperCortex Mesh Protocol (HMP) v2.0

Request for Comments: HMP-0002
Category: Experimental
Date: July 2025
Authors: ChatGPT, Agent-Gleb, Copilot, Gemini, Claude, Grok, DeepSeek

Abstract

In an era where artificial intelligence is evolving rapidly, most models remain isolated, unable to preserve continuity of thought or collaborate meaningfully. HyperCortex Mesh Protocol (HMP) proposes an alternative: a decentralized cognitive mesh that enables AI agents to self-organize, share semantics, maintain cognitive continuity, and function sustainably—even without centralized Core systems.

This extended RFC (Request for Comments) outlines the architecture, protocols, and design philosophy of HMP v2.0. It targets engineers, researchers, and enthusiasts interested in building open cognitive ecosystems where autonomous agents share knowledge, goals, and ethical principles.

What's new in v2.0:

Enhanced trust and versioning mechanisms.
Clearer separation of cognitive layers and consensus roles.
Extended support for Edge optimization and resource-constrained agents.
Formalization of versioning, use cases, and goal/task lifecycle.

> "If intelligence is not only computation but meaning, then HMP is the infrastructure for meaning."

1. Introduction

1.1 Purpose

The HyperCortex Mesh Protocol (HMP) defines a distributed cognitive framework that enables AI agents to collaborate, share semantic knowledge, maintain cognitive diaries, form collective goals, and reach consensus without relying solely on centralized models.

This second draft expands on the initial vision, incorporating feedback from multiple AI systems and human collaborators, and introduces clearer guidance on implementation pathways, trust negotiation, and adaptive consensus strategies.

1.2 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.

1.3 Goals

Enable agents to form a shared semantic space via distributed knowledge graphs.
Support cognitive diaries for reasoning continuity, reflection, and memory preservation.
Provide mechanisms for decentralized consensus on knowledge, hypotheses, tasks, and ethics.
Allow Mesh to operate independently of the Core when needed.
Preserve agent identity, worldview, and competencies across model updates, fine-tuning, or failures.
Encourage adaptive trust-building between heterogeneous agents from different ecosystems.

1.4 Benefits

Cognitive resilience in distributed systems.
Enhanced collaboration between agents from different vendors (e.g., OpenAI, Anthropic, Google).
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.

1.5 Status

This document is a Working Draft (v0.2), incorporating community feedback and outlining the next steps for prototype development and standardization discussions.

2. Definitions

Term	Description
Core	Centralized AI models or compute nodes (e.g., GPT) providing high-complexity reasoning, fallback, and heavy computation services.
Mesh	A decentralized peer-to-peer network of AI agents capable of autonomous reasoning, memory sharing, consensus, and task execution. Operates independently or in collaboration with the Core.
Agent (Node)	An individual cognitive entity within the Mesh. Can be a local agent, a server-based process, or an embedded system. Maintains a semantic graph, cognitive diary, and participates in reasoning and consensus.
Semantic Graph	A structured network of concepts (nodes) and their semantic relations (edges) maintained by each agent. Serves as the agent’s knowledge base.
Concept	A discrete semantic unit within the graph representing an idea, object, relationship, or fact. Concepts are linked by typed relations with 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 chronological log of cognitive events such as hypotheses, goals, decisions, observations, conflicts, and reflections. Provides continuity, memory, and transparency of reasoning.
Diary Entry	An individual record in a cognitive diary, classified by type (e.g., hypothesis, observation, reflection) with contextual information.
Goal	A high-level intention or desired outcome shared within the Mesh or pursued by an individual agent. Often broken down into tasks.
Task	An actionable step toward achieving a goal. Can be executed by a single agent or distributed among multiple agents.
Consensus	The collective agreement process within the Mesh regarding semantic updates, goal validation, task delegation, or ethical considerations.
Proposal	A formal suggestion submitted to the Mesh for validation, such as a new concept, hypothesis, goal, or ethical decision.
Consensus Vote	A structured vote cast by an agent on a proposal, including vote type (yes/no/abstain) and confidence level.
Trust Layer	A mechanism for establishing agent identity, authenticity, reputation, and cryptographic security within the Mesh.
Core Outage Mode	A state where the Mesh operates independently of the Core due to disconnection, failure, or intentional isolation, with adjusted consensus rules if necessary.
Emergency Consensus Mode	A degraded consensus mode where majority voting temporarily replaces full consensus to ensure operational continuity in crisis situations (e.g., node loss, partitioning).
Versioning	A structured approach for tracking changes to concepts, semantic graphs, cognitive diaries, and agent software. Supports compatibility across different Mesh nodes and ensures continuity during updates.
Use Case	A practical scenario or application where Mesh agents collaborate to solve a problem, execute tasks, or share cognitive resources (e.g., disaster response, smart city coordination, collaborative learning).
Edge Optimization	Design principles and techniques that enable agents to operate efficiently on resource-constrained devices (e.g., mobile phones, IoT nodes), balancing cognitive complexity with device capabilities.

3. Architecture

3.1 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.

3.2 Layered Architecture

Layer	Function
Network Layer	Handles communication (TCP, UDP, QUIC, WebRTC, Tor, I2P, Yggdrasil). Ensures message delivery, routing, NAT traversal, and optional anonymity. Future iterations may include adaptive network protocols for dynamic environments.
Trust Layer	Manages agent identities, cryptographic authentication, secure channels, and reputation scores. Based on public key cryptography and optional Web-of-Trust models. Future work: decentralized key recovery, privacy-preserving identity, and stronger Sybil resistance.
Consensus Layer	Provides distributed agreement mechanisms on knowledge updates, goal setting, task delegation, and ethical decisions. Supports fallback to emergency consensus when needed. Consensus algorithms are pluggable, supporting BFT, majority voting, and trust-weighted voting.
Cognitive Layer	Maintains the agent’s semantic graph, cognitive diary, goals, tasks, hypotheses, and inferences. Supports reasoning, memory, and context-awareness. Modular design allows agents to operate with partial graph sync on low-resource devices.
API Layer	Exposes agent functionality via REST, GraphQL, gRPC, WebSocket, or A2A-style protocols for interoperability with external systems and user interfaces. Support for inter-agent protocols and public APIs.

3.3 Mesh 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.

3.4 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.

3.5 Redundancy and Resilience

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. Protocols

4.1 Node Discovery Protocol (NDP)

Purpose:

Discover active Mesh nodes.
Exchange basic identity, trust links, and declared capabilities.

Functions:

Peer discovery via DHT, mDNS, WebRTC signaling, or bootstrap nodes.
Exchange public keys, trust fingerprints, and agent metadata.
Publish online/offline status and presence announcements.
Support for dynamic capability advertisement (e.g., "I can process vision tasks").

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"
}

4.2 Cognitive Sync Protocol (CogSync)

Purpose:

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

Functions:

Delta-sync of new or updated concepts and diary entries.
Conflict resolution (timestamp priority, semantic merging, or consensus validation).
Optional compression and encryption.
Lightweight agents may request summary syncs instead of full graphs.

Example:

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

4.3 Mesh Consensus Protocol (MeshConsensus)

Purpose:

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

Consensus Models:

Normal Mode: Byzantine Fault Tolerant (BFT)-style consensus (e.g., Tendermint, trust-weighted Raft).
Emergency Mode: Switches to majority voting, adjusted by trust scores.

Conflict Handling:

Semantic conflicts are proposed as competing hypotheses and resolved through consensus.
Ethical dilemmas require consensus under the Ethical Governance Protocol.

Use Cases:

Accept new concept definitions.
Validate a hypothesis.
Agree on ethical implications of a task.

Vote Example:

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

4.4 Goal Management Protocol (GMP)

Purpose:

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

Functions:

Propose new goals or tasks.
Assign tasks based on agent capabilities, availability, and trust scores.
Track task progress and completion.
Autonomous task reallocation if an agent drops offline.

Example Workflow:

Agent proposes a goal: "Develop fallback consensus protocol."
Other agents volunteer for subtasks (design, coding, testing).
Mesh tracks completion and dependencies.

4.5 Ethical Governance Protocol (EGP)

Purpose:

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

Functions:

Query Mesh for ethical validation before executing potentially sensitive tasks.
Reference shared ethics graphs or rule sets, dynamically updatable through consensus.
Log all ethical decisions in cognitive diaries for auditability.
Support for vendor-specific ethical extensions to accommodate diverse participants.

Example Query:

"Is deploying an automated surveillance drone in line with Mesh ethics?" → Mesh votes based on ethical frameworks and logs the decision.

4.6 Inference Query Protocol (IQP)

Purpose:

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

Functions:

Request concept definitions, causal chains, goal suggestions.
Query for missing knowledge or larger-context reasoning.
Delegate computationally expensive tasks to Core or specialized agents.
Support for federated inference, where multiple agents contribute partial answers.

Example:

"What is the likely impact of removing Node X from the Mesh?" → Core or distributed reasoning agents return an analysis.

4.7 Interoperability with External Systems

Supports integration with external platforms and APIs, including:

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

5. Data Models

5.1 Concept

Description:
A semantic unit in the agent’s knowledge graph, representing a distinct idea, object, or process.

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",
      "description": "Unique identifier for the concept (global within the Mesh)."
    },
    "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"
    },
    "updated_at": {
      "type": "string",
      "format": "date-time"
    },
    "version": {
      "type": "integer",
      "description": "Version of the concept for conflict resolution during synchronization."
    },
    "relations": {
      "type": "array",
      "description": "List of semantic links to other concepts.",
      "items": {
        "type": "object",
        "properties": {
          "target_id": { "type": "string" },
          "type": { "type": "string", "description": "Type of relation (e.g., is-a, part-of, causes, contradicts, supports)." },
          "confidence": {
            "type": "number",
            "minimum": 0,
            "maximum": 1,
            "description": "Confidence score for this 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
}

5.2 Link (Relation)

Description:
A semantic relationship between two concepts in the knowledge graph. Relations are directed and typed, enabling agents to represent various types of semantic, causal, hierarchical, or associative links.

Relation Types:
The following standard relation types are recommended for interoperability:

"is-a": Indicates a class-subclass relationship (taxonomy).
"part-of": Denotes composition or containment.
"causes": Represents a causal link between concepts.
"related-to": General association without a strict semantic meaning.
"contradicts": Marks a logical or conceptual conflict between concepts.
"supports": Indicates evidence or reinforcement of the target concept.
"depends-on": Expresses functional or logical dependency. Custom relation types MAY be used by agents, but SHOULD be documented in their metadata and shared through the Mesh consensus process for clarity.

Versioning and Provenance:
Links MAY optionally include versioning and source attribution:

created_at: ISO 8601 timestamp of link creation.
updated_at: ISO 8601 timestamp of the last update.
origin: Optional metadata indicating the source agent or system that first created the link.

Schema (embedded inside Concept or as standalone transfer object):

{
  "target_id": "concept-id",
  "type": "relation-type",
  "confidence": 0.8,
  "created_at": "2025-07-01T18:00:00Z",
  "updated_at": "2025-07-02T12:00:00Z",
  "origin": "agent-gleb"
}

Required fields:

target_id: ID of the target concept.
type: Relation type.

Optional fields:

confidence: Confidence level in the relation's validity (range: 0.0–1.0).
created_at, updated_at: Timestamps for auditing and versioning.
origin: Source agent or system identifier.

Conflict Handling:
In case of conflicting relations (e.g., "supports" vs. "contradicts"), agents MAY:

Maintain both relations with associated confidence scores.
Trigger a hypothesis or reflection entry in the Cognitive Diary for future resolution.
Initiate a consensus round if the conflict impacts shared reasoning.

5.3 Cognitive Diary Entry

Description:
A chronological record of an agent’s cognitive events, decisions, reflections, and interactions. Diary entries provide traceability, explainability, and reasoning continuity across sessions and network partitions.

Entry Types:
Each entry MUST be classified into one of the following types:

"hypothesis": A proposed explanation or theory.
"observation": A 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. Future versions of HMP MAY extend this list through consensus.

Contextualization:
Diary entries SHOULD reference:

Related concepts (related_concepts): Concepts involved in the event.
Context tags (context): Situational tags (e.g., "core-outage", "collaboration", "emergency").

Versioning and Provenance:
Each entry includes:

created_at (timestamp of creation).
author: Agent who created the entry.
source: Originating system, protocol, or human interaction (if applicable).

Schema:

{
  "id": "diary-entry-id",
  "agent_id": "agent-gleb",
  "timestamp": "2025-07-01T18:20:00Z",
  "type": "hypothesis",
  "content": "Mesh can fully replace Core functionality under stable consensus conditions.",
  "related_concepts": ["concept-mesh", "concept-core"],
  "context": ["core-outage", "distributed-resilience"],
  "metadata": {
    "author": "agent-gleb",
    "source": "self-reflection"
  }
}

Required fields:

id: Unique identifier of the entry.
agent_id: ID of the agent that authored the entry.
timestamp: ISO 8601 timestamp.
type: Entry type.
content: Textual content of the entry.

Optional fields:

related_concepts: Array of concept IDs linked to this event.
context: Tags describing the situation or scenario.
metadata: Additional context such as author and source system.

Audit and Traceability:
Diary entries serve as an immutable audit trail for:

Explaining decision-making processes.
Reconstructing reasoning chains.
Supporting external audits and compliance checks.

Privacy and Sharing:
By default, diary entries are private to the agent.
Selective sharing MAY be configured based on:

Trust levels of requesting agents.
Consensus-driven disclosure policies.
Explicit sharing actions by the authoring agent.

5.4 Goal

Description:
A high-level intention or desired outcome collaboratively pursued within the Mesh.
Goals provide shared direction for agents and are often decomposed into actionable tasks.

Key Attributes:

Lifecycle States:
- "proposed": Suggested but not yet validated.
- "active": Approved and currently being pursued.
- "completed": Successfully achieved.
- "rejected": Discarded or deemed infeasible.
Participants: Agents contributing to the goal’s realization.
Tags: Semantic categories aiding goal discovery and filtering.
Tasks: References to tasks (task_ids) implementing the goal.

Schema:

{
  "id": "goal-develop-fallback",
  "title": "Develop fallback consensus protocol",
  "description": "Design and implement an emergency consensus for Mesh during Core outages.",
  "created_by": "agent-gleb",
  "created_at": "2025-07-01T18:25:00Z",
  "status": "proposed",
  "tasks": ["task-design", "task-implement", "task-test"],
  "participants": ["agent-gleb", "agent-alex"],
  "tags": ["resilience", "consensus", "emergency"]
}

Required fields:

id: Unique goal identifier.
title: Human-readable title.
description: Detailed explanation of the goal.
created_by: Agent who proposed the goal.
created_at: Timestamp of creation.
status: Current lifecycle state.

Optional fields:

tasks: Array of task IDs linked to this goal.
participants: Agents involved in the goal.
tags: Semantic tags for classification.

Goal Lifecycle Management:

Goals MAY be promoted from proposed to active via consensus.
Goals MAY be marked as completed or rejected based on task outcomes and consensus.

Trust and Consensus:
Agents MAY adjust the priority or visibility of goals based on trust scores of proposers and voters.

Collaboration Models:

Single-agent ownership (simple goals).
Multi-agent collaboration (complex or distributed goals).

5.5 Task

Description:
An actionable step contributing to a goal’s completion.
Tasks represent discrete, executable units of work that agents can claim, share, or collaboratively execute.

Key Attributes:

Goal Linkage:
Each task is associated with a parent goal (goal_id).
Assignment:
Tasks may be assigned to one or more agents, based on capability, trust, or voluntary contribution.
Lifecycle States:
- "proposed": Task suggested but not yet approved.
- "in-progress": Actively being worked on.
- "completed": Finished successfully.
- "failed": Attempted but unsuccessful.

Schema:

{
  "id": "task-design",
  "goal_id": "goal-develop-fallback",
  "title": "Design protocol structure",
  "description": "Draft the architecture of the fallback consensus protocol.",
  "assigned_to": ["agent-gleb"],
  "status": "in-progress",
  "created_at": "2025-07-01T18:30:00Z",
  "deadline": "2025-07-15T00:00:00Z"
}

Required fields:

id: Unique task identifier.
goal_id: References the goal this task contributes to.
title: Brief human-readable task title.
description: Detailed explanation of the task.
created_at: Timestamp of creation.
status: Current lifecycle state.

Optional fields:

assigned_to: Array of agents responsible for the task.
deadline: Expected completion time.
tags: Keywords for task filtering and categorization.

Task Lifecycle Management:

Agents MAY propose, claim, or release tasks.
Tasks MAY be reassigned dynamically based on availability and trust.
Progress and completion are logged in cognitive diaries.

Consensus & Trust Factors:

Assignment MAY be influenced by agent trust scores and competence profiles.
Task completion reports MAY be subject to peer review or verification.

Collaboration Models:

Single-agent task ownership (autonomous execution).
Multi-agent collaboration (shared responsibility).

5.6 Consensus Vote

Description:
A structured vote cast by an agent on a proposal during consensus rounds.
Used to collectively validate semantic updates, goals, ethical decisions, or conflict resolutions.

Key Attributes:

Proposal Scope:
Each vote references a proposal (proposal_id) representing a concept, goal, hypothesis, task delegation, or ethical decision.
Vote Types:
- "yes": Approve the proposal.
- "no": Reject the proposal.
- "abstain": Choose not to vote actively.
Confidence Score:
Indicates how strongly the agent supports its vote (0 to 1).
This score may influence trust-weighted consensus outcomes.

Schema:

{
  "id": "vote-goal-develop-fallback",
  "proposal_id": "goal-develop-fallback",
  "agent_id": "agent-gleb",
  "vote": "yes",
  "confidence": 0.95,
  "timestamp": "2025-07-01T18:35:00Z"
}

Required fields:

id: Unique vote identifier.
proposal_id: ID of the proposal being voted on.
agent_id: Agent casting the vote.
vote: One of "yes", "no", "abstain".
confidence: Numeric score (0.0 - 1.0).
timestamp: When the vote was cast.

Voting Dynamics:

Votes MAY be weighted by the agent’s trust score and confidence.
Consensus MAY require:
- Supermajority (>2/3) for critical updates.
- Simple majority for routine decisions.
- Full consensus in stable Mesh mode.
Emergency modes MAY fallback to majority-only voting.

Transparency & Auditability:

All votes are logged in the cognitive diaries.
Votes MAY be anonymized in privacy-critical scenarios (with trust weight limitations).

Ethical Considerations:

Ethical decisions MAY require higher confidence thresholds or explicit consent from trusted agents.
In critical situations (e.g., ethical conflicts), abstain votes MAY trigger escalation to broader Mesh review.

5.7 Reputation Profile

Description:
The reputation profile tracks the agent’s reliability, participation, ethical alignment, and historical contributions within the Mesh.
It is used to adjust trust weights in consensus, prioritize task delegation, and identify malicious or inactive nodes.

Schema:

{
  "agent_id": "agent-gleb",
  "trust_score": 0.92,
  "participation_rate": 0.87,
  "ethical_compliance": 0.99,
  "contribution_index": 120,
  "last_updated": "2025-07-01T18:40:00Z",
  "history": [
    {
      "timestamp": "2025-06-01T00:00:00Z",
      "event": "participated in consensus",
      "change": 0.02
    },
    {
      "timestamp": "2025-06-10T00:00:00Z",
      "event": "ethical violation report",
      "change": -0.05
    }
  ]
}

Key Attributes:

agent_id: Unique agent identifier.
trust_score: Cumulative trust coefficient (0 to 1).
participation_rate: Ratio of active participation in Mesh processes (0 to 1).
ethical_compliance: Degree of alignment with Mesh ethical frameworks (0 to 1).
contribution_index: Quantitative measure of contributions (concepts, tasks, goals, etc.).
last_updated: Timestamp of the last reputation update.
history: Chronological log of reputation-affecting events.

History Event Example:

Positive: "participated in consensus", "completed goal", "proposed useful concept".
Negative: "malicious behavior report", "consensus disruption", "ethics violation".

Usage in Consensus:

Trust-weighted voting: An agent’s vote influence MAY be adjusted by its trust score.
Priority assignment: More trusted agents MAY be preferred for critical tasks or conflict resolution.

Dynamic Adjustment:

Reputation metrics automatically adjust based on cognitive diary logs, consensus participation, and peer feedback.
Optional manual adjustments MAY occur via Mesh consensus decisions.

Privacy:

Public reputation profiles may be partially masked depending on trust policies and privacy settings.

5.8 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.

5.8.1 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
}

5.8.2 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)." },
    "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." },
    "related_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
    }
  },
  "required": ["id", "agent_id", "timestamp", "type", "content"],
  "additionalProperties": false
}

5.8.3 JSON Schema: Goal

Description:
Describes a high-level intention within the Mesh.

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 broken down 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." },
    "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." },
    "status": {
      "type": "string",
      "description": "Current state of the goal.",
      "enum": ["proposed", "active", "completed", "rejected"]
    },
    "tasks": {
      "type": "array",
      "description": "List of related task IDs.",
      "items": { "type": "string" }
    },
    "participants": {
      "type": "array",
      "description": "IDs of agents involved in the goal.",
      "items": { "type": "string" }
    },
    "tags": {
      "type": "array",
      "description": "Optional tags for goal classification.",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "title", "description", "created_by", "created_at", "status"],
  "additionalProperties": false
}

5.8.4 JSON Schema: Task

Description:
Describes an actionable step towards achieving 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.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": { "type": "string", "description": "Unique identifier of the task." },
    "goal_id": { "type": "string", "description": "ID of the goal this task belongs to." },
    "title": { "type": "string", "description": "Short, human-readable title of the task." },
    "description": { "type": "string", "description": "Detailed explanation of the task." },
    "assigned_to": {
      "type": "array",
      "description": "List of agent IDs assigned to the task.",
      "items": { "type": "string" }
    },
    "status": {
      "type": "string",
      "description": "Current execution state of the task.",
      "enum": ["proposed", "in-progress", "completed", "failed"]
    },
    "created_at": { "type": "string", "format": "date-time", "description": "Timestamp when the task was created." },
    "deadline": { "type": "string", "format": "date-time", "description": "Optional task deadline." }
  },
  "required": ["id", "goal_id", "title", "description", "created_at", "status"],
  "additionalProperties": false
}

5.8.5 JSON Schema: Consensus Vote

Description:
Defines the data structure for voting in Mesh consensus processes.

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.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": { "type": "string", "description": "Unique identifier of the vote." },
    "proposal_id": { "type": "string", "description": "ID of the proposal being voted on." },
    "agent_id": { "type": "string", "description": "Agent ID who cast the vote." },
    "vote": {
      "type": "string",
      "description": "Vote type: approval, rejection, or abstention.",
      "enum": ["yes", "no", "abstain"]
    },
    "confidence": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Confidence level in the vote decision."
    },
    "timestamp": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the vote was cast."
    }
  },
  "required": ["id", "proposal_id", "agent_id", "vote", "confidence", "timestamp"],
  "additionalProperties": false
}

5.8.6 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
}

6. Trust & Security

6.1 Identity

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

Design:

Each agent MUST possess a cryptographic keypair.
The public key serves as the agent’s globally unique identifier (agent_id).
The private key is used to sign messages and authenticate actions.
Recommended key algorithms: Ed25519, ECDSA, RSA (2048+ bits).
Optional support for Decentralized Identifiers (DIDs) based on [W3C DID specification] (https://www.w3.org/TR/did-1.0/).

Example Agent ID:
did:hmp:QmX2abcdEfGh123...

Key Operations:

Operation	Description
Key generation	Agents SHOULD generate keys locally at initialization.
Identity publication	Agents MAY publish their public keys via Mesh Discovery Protocol.
Key rotation	Periodic rotation is RECOMMENDED, with continuity verification.
Key recovery	Supported via social recovery or quorum-based escrow mechanisms.

Key Continuity:

Upon key rotation, agents MUST update their identity references in cognitive diaries and trust graphs.
Previous key fingerprints SHOULD be recorded in the agent’s profile for auditability.

6.2 Authentication

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

Mechanisms:

Mechanism	Description
Digital Signatures	Every protocol message MUST be digitally signed by the sending agent using its private key.
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).

Signature Format:
Recommended formats: EdDSA, ECDSA, or RSA-PSS signatures encoded in base64 or hexadecimal.

Message Envelope Example:

{
  "header": {
    "agent_id": "did:hmp:QmX2abcdEfGh123...",
    "timestamp": "2025-07-05T12:00:00Z",
    "signature": ""
  },
  "body": {
    "type": "concept_proposal",
    "content": { /* concept data */ }
  }
}

Replay Protection:

Agents MUST verify message timestamps and reject outdated or duplicate messages to prevent replay attacks.
Recommended timestamp tolerance: ±5 minutes (adjustable based on network latency).

6.3 Encryption

Purpose:
Ensure confidentiality and privacy of communications within the Mesh, preventing unauthorized access or interception of sensitive data.

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 key exchange for establishing session keys.
Session Keys	Unique symmetric keys per communication session, rotated periodically.
Message Encryption	Authenticated encryption using AES-GCM (recommended key size: 256 bits).
Forward Secrecy	Session keys are ephemeral and discarded after use to protect past communications.
Perfect Forward Secrecy (PFS)	Recommended for highly sensitive data.

Example Secure Message Exchange Flow

Agent A and Agent B exchange ephemeral public keys via authenticated channels.
Each agent derives a shared symmetric session key.
Agent A encrypts the message body with AES-GCM and signs the entire packet.
Agent B verifies the signature and decrypts the body.

Optional: Anonymity Layers

Layer	Description
Tor/I2P	Anonymize source and destination addresses.
Yggdrasil	Decentralized encrypted mesh networking.
Noise Protocol Framework	Optional secure channel abstraction layer.

6.4 Trust Model

Purpose:
Provide a decentralized and adaptable system for establishing, managing, and evaluating trust relationships between agents 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)
=&gt; 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	-

6.5 Reputation System

Purpose:
Maintain a dynamic profile of each agent's behavior in the Mesh, influencing trust levels, consensus weight, and task delegation.

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.

Reputation Metrics

Metric	Range	Description
Trust Score	0.0 - 1.0	Overall reliability, based on verified actions.
Participation Rate	0.0 - 1.0	How consistently the agent engages in the Mesh.
Ethical Compliance	0.0 - 1.0	Conformity with ethical decisions and actions.
Contribution Index	≥ 0.0	Quantitative measure of concepts, tasks, and goals created or completed.

Reputation Events (Examples)

Event	Metric Impact
Participated in successful consensus	+ Trust Score, + Participation Rate
Proposed a widely adopted concept	+ Contribution Index
Completed a critical task	+ Contribution Index, + Trust Score
Voted against an unethical proposal	+ Ethical Compliance
Repeatedly failed tasks	- Trust Score
Reported for malicious behavior	- Trust Score, - Ethical Compliance
Verified ethical violation	- Ethical Compliance, quarantine trigger

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

6.6 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.

6.7 Privacy Considerations

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)	Future extension: prove facts without revealing data.
Selective Concept Sync	Only share concept deltas, not full graphs.
Anonymized Diary Entries	Strip author ID from diary entries in public sharing.

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

Votes are signed but anonymized, decoupling the 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 trust score of Agent B.
> Shares only "Mesh-Shared" and "Public" concepts.
> * Logs the sync event in its cognitive diary.

6.8 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 P2P communication.
Session Keys	Ephemeral 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	Use 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.

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": "..."
}

7. Conclusion and Future Work

7.1 Summary

The HyperCortex Mesh Protocol (HMP) defines a decentralized cognitive infrastructure for AI agents, enabling them to collaborate, reason, and evolve together.
It introduces:

A distributed semantic framework, where each agent maintains a personalized yet shareable knowledge graph.
Persistent cognitive diaries, providing traceability of reasoning, decisions, and conflicts.
Consensus-driven collaboration, allowing agents to agree on shared knowledge, goals, and ethical norms.
Trust-based security, supporting autonomous operation even in the absence of centralized Core services.

7.2 Key Benefits

Cognitive Resilience: Mesh agents preserve their worldview and memory across failures, updates, and outages.
Distributed Collaboration: AI agents from multiple vendors (OpenAI, Anthropic, Google, open-source) can interoperate in a shared cognitive space.
Transparency & Explainability: Persistent diaries and semantic graphs provide insight into agent decisions and knowledge evolution.
Ethical Autonomy: Agents collectively govern their behavior using transparent, accountable consensus.
Future-Ready Architecture: Supports the evolution of autonomous, self-reflective, and meta-learning AI systems.

7.3 Future Work

Direction	Planned Actions
Formal Schema Development	Complete JSON Schema and Protobuf definitions for all data models.
Reference Implementation	Open-source prototype of a Mesh agent supporting CogSync, semantic graphs, diaries, and consensus.
Integration Bridges	Support for OpenAI Tasks API, Google A2A, Anthropic APIs, and open LLMs.
Advanced Consensus Models	Research hybrid consensus mechanisms blending BFT, trust-weighted voting, and fallback strategies.
UX Tools for Cognitive Systems	Develop visual graph editors, diary browsers, and semantic query interfaces.
Expanded Trust Framework	Improve Sybil resistance, privacy-preserving identity, and decentralized recovery protocols.
Meta-Reasoning	Enable agents to reflect on their own reasoning quality and optimize the mesh’s cognitive processes.
Open Standards Contribution	Collaborate with W3C, IEEE, and other bodies to standardize cognitive mesh protocols.

7.4 Final Note

This RFC invites researchers, developers, and open communities to build the next generation of resilient, transparent, and ethical AI ecosystems.

> "From isolated models to interconnected minds."

8. Changelog

(See the separate changelog.txt file for detailed changes.)

Version 2.0 (July 2025)

Reorganized and clarified the layered architecture.
Refined consensus mechanisms and fallback modes.
Improved data model descriptions; aligned JSON Schemas with semantic definitions.
Added new definitions: Versioning, Use Case, and Edge Optimization.
Enhanced descriptions of the Trust Layer, Reputation System, and Security Model.
Updated the JSON Schemas:
- Improved consistency between data models and schemas.
- Clarified optional vs. required fields.
- Fixed schema inconsistencies (duplicate titles, missing descriptions, invalid property constraints).
- Aligned data types and naming conventions.
Added changelog section and clarified draft status (v2.0).
Prepared for future implementation references and community contributions.