Jigar Patel

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AutoResearchClaw Integration

avatar for Jigar PatelJigar Patel
1 min read

Objective

Use an autoresearch loop for engineering tasks where broad hypothesis space exists but execution needs deterministic checkpoints.

Design

  • Hypothesis queue: candidate tasks are normalized, prioritized, and deduplicated.
  • Execute phase: each cycle runs bounded experiments or analysis jobs with resource caps.
  • Verify phase: outputs must include source evidence before promotion.
  • Selection phase: keep/discard based on objective metrics (quality, reproducibility, cost).
  • Persistence: full state machine written to a lightweight datastore for auditability.

Implementation

  • Wrapped loop orchestration around existing task tools instead of replacing them.
  • Added deterministic stopping criteria to avoid infinite loops.
  • Attached scoring rubric to outputs (clarity, correctness, cost, and reproducibility).
  • Exposed a narrow admin surface for manual override and safety breaks.

Operational Notes

This integration is most useful for non-time-critical R&D tasks: market scans, proposal drafting, and architecture alternatives. For production changes, it stays bounded by human review gates.

Code