bzzz/old-docs/UNIFIED_DEVELOPMENT_PLAN.md

Project Bzzz & HMMM: Integrated Development Plan

1. Unified Vision

This document outlines a unified development plan for Project Bzzz and its integrated meta-discussion layer, Project HMMM. The vision is to build a decentralized task execution network where autonomous agents can not only act but also reason and collaborate before acting.

• Bzzz provides the core P2P execution fabric (task claiming, execution, results).
• HMMM provides the collaborative "social brain" (task clarification, debate, knowledge sharing).

By developing them together, we create a system that is both resilient and intelligent.

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2. Core Architecture

The combined architecture remains consistent with the principles of decentralization, leveraging a unified tech stack.

Component	Technology	Purpose
Networking	libp2p	Peer discovery, identity, and secure P2P communication.
Task Management	GitHub Issues	The single source of truth for task definition and atomic allocation via assignment.
Messaging	libp2p Pub/Sub	Used for both bzzz (capabilities) and hmmm (meta-discussion) topics.
Logging	Hypercore Protocol	A single, tamper-proof log stream per agent will store both execution logs (Bzzz) and discussion transcripts (HMMM).

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3. Key Features & Refinements

3.1. Task Lifecycle with Meta-Discussion

The agent's task lifecycle will be enhanced to include a reasoning step:

1. Discover & Claim: An agent discovers an unassigned GitHub issue and claims it by assigning itself.
2. Open Meta-Channel: The agent immediately joins a dedicated pub/sub topic: bzzz/meta/issue/{id}.
3. Propose Plan: The agent posts its proposed plan of action to the channel. e.g., "I will address this by modifying file.py and adding a new function x()."
4. Listen & Discuss: The agent waits for a brief "objection period" (e.g., 30 seconds). Other agents can chime in with suggestions, corrections, or questions. This is the core loop of the HMMM layer.
5. Execute: If no major objections are raised, the agent proceeds with its plan.
6. Report: The agent creates a Pull Request. The PR description will include a link to the Hypercore log containing the full transcript of the pre-execution discussion.

3.2. Safeguards and Structured Messaging

• Combined Safeguards: Hop limits, participant caps, and TTLs will apply to all meta-discussions to prevent runaway conversations.

• Structured Messages: To improve machine comprehension, meta_msg payloads will be structured.

  {
    "type": "meta_msg",
    "issue_id": 42,
    "node_id": "bzzz-07",
    "msg_id": "abc123",
    "parent_id": null,
    "hop_count": 1,
    "content": {
      "query_type": "clarification_needed",
      "text": "What is the expected output format?",
      "parameters": { "field": "output_format" }
    }
  }
  

3.3. Human Escalation Path

• A dedicated pub/sub topic (bzzz/meta/escalation) will be used to flag discussions requiring human intervention.
• An N8N workflow will monitor this topic and create alerts in a designated Slack channel or project management tool.

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4. Integrated Development Milestones

This 8-week plan merges the development of both projects into a single, cohesive timeline.

Week	Core Deliverable	Key Features & Integration Points
1	P2P Foundation & Logging	Establish the core agent identity and a unified Hypercore log stream for both action and discussion events.
2	Capability Broadcasting	Agents broadcast capabilities, including which reasoning models they have available (e.g., claude-3-opus).
3	GitHub Task Claiming & Channel Creation	Implement assignment-based task claiming. Upon claim, the agent creates and subscribes to the meta-discussion channel.
4	Pre-Execution Discussion	Implement the "propose plan" and "listen for objections" logic. This is the first functional version of the HMMM layer.
5	Result Workflow with Logging	Implement PR creation. The PR body must link to the Hypercore discussion log.
6	Full Collaborative Help	Implement the full task_help_request and meta_msg response flow, respecting all safeguards (hop limits, TTLs).
7	Unified Monitoring	The Mesh Visualizer dashboard will display agent status, execution logs, and live meta-discussion transcripts.
8	End-to-End Scenario Testing	Conduct comprehensive tests for combined scenarios: task clarification, collaborative debugging, and successful escalation to a human.

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5. Conclusion

By integrating HMMM from the outset, we are not just building a distributed task runner; we are building a distributed reasoning system. This approach will lead to a more robust, intelligent, and auditable Hive, where agents think and collaborate before they act.