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[ROADMAP] Hermes Agent Full Local Competency Vision #199

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opened 2026-03-31 22:00:05 +00:00 by Timmy · 0 comments
Timmy commented 2026-03-31 22:00:05 +00:00
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Vision and Backlog Population Plan for Full Local Competency

Goal

Populate the backlog with a multi-month roadmap to transition to a fully sovereign Hermes AI agent. The focus is on local-first operation, eliminating cloud dependencies, and maintaining competence and responsiveness.

Current Context / Assumptions

  • Hermes agent currently runs primarily on Anthropic and Gemini cloud models with local Ollama/LLaMA fallback.
  • TurboQuant and Gemini features are advancing, but full local inference and orchestration remain incomplete.
  • KimiClaw handles task decomposition and orchestration but requires enhancement for resilience, concurrency, and scale.
  • Robust pipelines are required for local data ingestion, model acceleration, multi-modal understanding, and art appreciation.

Proposed Approach

  • Systematically identify missing pieces to achieve fully local operation without degradation.
  • Break down missing work into manageable autonomous agent tasks.
  • Prioritize backlog items by impact on sovereignty, usability, and performance.
  • Integrate ongoing monitoring, benchmarking, and test coverage to validate progress.

Step-by-step Plan

  1. Backlog Mining and Validation

    • Analyze existing issues and pull requests.
    • Identify and document gaps in local inference and orchestration.
    • Validate dependencies on external clouds and plan removal or isolation.

  2. Local Model Enhancements

    • Expand Ollama/TurboQuant integration with quantized cache and QJL acceleration.
    • Port CUDA kernels related to QJL to Metal for Apple Silicon GPU acceleration.
    • Improve local model fallback, caching, and pruning strategies.

  3. Data Ingestion Pipeline

    • Build robust pipelines for local-first Twitter archive analysis and ingestion.
    • Implement media (image/audio/video) processing pipelines with KimiClaw task decomposition.
    • Develop art appreciation modules integrating multi-modal data for cultural insights.

  4. Orchestration and Autonomous Tasking

    • Enhance KimiClaw’s orchestration for dynamic task planning and breakdown.
    • Setup parallel autonomous agents for high-throughput execution and reporting.

  5. Infrastructure and Tooling

    • Harden Hermes internal infrastructure for fault tolerance and performance.
    • Enhance CLI tooling and sovereign user control mechanisms.
    • Expand monitoring, telemetry, and alerting dashboards.

  6. Cutover Planning and Rollout

    • Define detailed test plans, metrics, and success criteria.
    • Setup automated regression and performance tests.
    • Prepare staged rollout paths with rollback capabilities.

Files Likely to Change

  • run_agent.py, config.yaml, and hermes_cli/ for orchestration and local provider integration.
  • tinker-atropos/, rust/ for local model acceleration and PyO3 extensions.
  • skills/ and tools/ for data ingestion, multi-modal analysis, and orchestration logic.
  • CI/CD pipelines and benchmark scripts.

Tests and Validation

  • Adding comprehensive unit and integration tests for all new and modified components.
  • Establishing benchmarks for latency, throughput, memory footprint, and accuracy.
  • Regression tests to ensure sovereignty and privacy are preserved.

Acceptance Criteria

  • Demonstrated local-only operation for core features without cloud fallback.
  • Performance benchmarks meet or exceed baseline metrics.
  • Complete test coverage with no unexplainable test failures.
  • Autonomous agents efficiently decompose and resolve complex tasks.
  • Pipeline successfully processes multi-modal data including social media, videos, and audio.
  • Clear rollback plan and detailed cutover documentation.

Risks and Open Questions

  • Complexities in Metal GPU kernel porting and build environment management.
  • Managing large local datasets securely and efficiently.
  • Balancing task concurrency with resource limits to avoid rate limits.
  • Ensuring long-term maintenance of multi-agent orchestration framework.

Document authored by Timmy on 2026-03-30 for backlog population and roadmap.

# Vision and Backlog Population Plan for Full Local Competency ## Goal Populate the backlog with a multi-month roadmap to transition to a fully sovereign Hermes AI agent. The focus is on local-first operation, eliminating cloud dependencies, and maintaining competence and responsiveness. ## Current Context / Assumptions - Hermes agent currently runs primarily on Anthropic and Gemini cloud models with local Ollama/LLaMA fallback. - TurboQuant and Gemini features are advancing, but full local inference and orchestration remain incomplete. - KimiClaw handles task decomposition and orchestration but requires enhancement for resilience, concurrency, and scale. - Robust pipelines are required for local data ingestion, model acceleration, multi-modal understanding, and art appreciation. ## Proposed Approach - Systematically identify missing pieces to achieve fully local operation without degradation. - Break down missing work into manageable autonomous agent tasks. - Prioritize backlog items by impact on sovereignty, usability, and performance. - Integrate ongoing monitoring, benchmarking, and test coverage to validate progress. ## Step-by-step Plan 1. **Backlog Mining and Validation** - Analyze existing issues and pull requests. - Identify and document gaps in local inference and orchestration. - Validate dependencies on external clouds and plan removal or isolation. 2. **Local Model Enhancements** - Expand Ollama/TurboQuant integration with quantized cache and QJL acceleration. - Port CUDA kernels related to QJL to Metal for Apple Silicon GPU acceleration. - Improve local model fallback, caching, and pruning strategies. 3. **Data Ingestion Pipeline** - Build robust pipelines for local-first Twitter archive analysis and ingestion. - Implement media (image/audio/video) processing pipelines with KimiClaw task decomposition. - Develop art appreciation modules integrating multi-modal data for cultural insights. 4. **Orchestration and Autonomous Tasking** - Enhance KimiClaw’s orchestration for dynamic task planning and breakdown. - Setup parallel autonomous agents for high-throughput execution and reporting. 5. **Infrastructure and Tooling** - Harden Hermes internal infrastructure for fault tolerance and performance. - Enhance CLI tooling and sovereign user control mechanisms. - Expand monitoring, telemetry, and alerting dashboards. 6. **Cutover Planning and Rollout** - Define detailed test plans, metrics, and success criteria. - Setup automated regression and performance tests. - Prepare staged rollout paths with rollback capabilities. ## Files Likely to Change - `run_agent.py`, `config.yaml`, and `hermes_cli/` for orchestration and local provider integration. - `tinker-atropos/`, `rust/` for local model acceleration and PyO3 extensions. - `skills/` and `tools/` for data ingestion, multi-modal analysis, and orchestration logic. - CI/CD pipelines and benchmark scripts. ## Tests and Validation - Adding comprehensive unit and integration tests for all new and modified components. - Establishing benchmarks for latency, throughput, memory footprint, and accuracy. - Regression tests to ensure sovereignty and privacy are preserved. ## Acceptance Criteria - Demonstrated local-only operation for core features without cloud fallback. - Performance benchmarks meet or exceed baseline metrics. - Complete test coverage with no unexplainable test failures. - Autonomous agents efficiently decompose and resolve complex tasks. - Pipeline successfully processes multi-modal data including social media, videos, and audio. - Clear rollback plan and detailed cutover documentation. ## Risks and Open Questions - Complexities in Metal GPU kernel porting and build environment management. - Managing large local datasets securely and efficiently. - Balancing task concurrency with resource limits to avoid rate limits. - Ensuring long-term maintenance of multi-agent orchestration framework. --- *Document authored by Timmy on 2026-03-30 for backlog population and roadmap.*
claude was assigned by allegro 2026-04-05 12:35:12 +00:00
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Reference: Timmy_Foundation/timmy-home#199
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