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docs: add adaptive agent selection problem document#389

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docs: add adaptive agent selection problem document#389
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agent/350-adaptive-agent-selection

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Add a new problem document exploring how fullsend can learn which agent/team/workflow configurations are effective for which classes of problems using evolutionary algorithms (GA) and Thompson Sampling.

The document covers:

  • Three optimization surfaces: team selection, agent configuration,
    and workflow shape
  • Candidate approaches: Thompson Sampling for discrete selection,
    GA for structured workflow evolution, and a hybrid
  • Fitness function design with gameability analysis and difficulty
    normalization
  • Context granularity trade-offs for bucketing fitness data
  • Cold-start strategies and similarity-based fallback
  • Shadow mode as a prerequisite for live deployment
  • Safety constraints preserving deterministic safety model
  • Storage and observability requirements
  • Minimum viable experiment design

Also links the new document from README.md.

Note: make lint could not run due to sandbox network restrictions (gitleaks hook download blocked). Pre-commit infrastructure error (exit 3), not a code issue.

Closes #350

Changed files

  • README.md
  • docs/problems/adaptive-agent-selection.md

Closes #350

Post-script verification

  • Branch is not main/master (agent/350-adaptive-agent-selection)
  • No protected paths modified
  • Secret scan passed (gitleaks — c1e8844bcd6b9900c031eb7b11e28ec4d576717d..HEAD)
  • Pre-commit hooks passed (authoritative run on runner)
  • Tests ran inside sandbox

Created by fullsend code agent

Add a new problem document exploring how fullsend can learn which
agent/team/workflow configurations are effective for which classes
of problems using evolutionary algorithms (GA) and Thompson Sampling.

The document covers:
- Three optimization surfaces: team selection, agent configuration,
  and workflow shape
- Candidate approaches: Thompson Sampling for discrete selection,
  GA for structured workflow evolution, and a hybrid
- Fitness function design with gameability analysis and difficulty
  normalization
- Context granularity trade-offs for bucketing fitness data
- Cold-start strategies and similarity-based fallback
- Shadow mode as a prerequisite for live deployment
- Safety constraints preserving deterministic safety model
- Storage and observability requirements
- Minimum viable experiment design

Also links the new document from README.md.

Note: make lint could not run due to sandbox network restrictions
(gitleaks hook download blocked). Pre-commit infrastructure error
(exit 3), not a code issue.

Closes #350
@fullsend-ai-coder fullsend-ai-coder Bot added the ready-for-review Agent PR ready for human review label Apr 23, 2026
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Preview: https://208bfbd2-site.fullsend-ai.workers.dev

Commit: 7bd494f8604fc065bab1cec8fce80988a25ce065

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Review: #389

Head SHA: 7bd494f
Timestamp: 2026-04-24T00:00:00Z
Outcome: approve

Summary

This PR adds a well-structured problem document exploring adaptive agent selection using evolutionary algorithms and Thompson Sampling, along with a README link. The document follows repo conventions — it presents multiple approaches with trade-offs, includes an extensive Open Questions section, and correctly cross-references five existing problem documents (all verified to exist). The change is tightly scoped to what issue #350 authorized. No code changes, no security implications. All internal links are valid. The document's treatment of safety constraints (deterministic safety preservation, self-referential fitness avoidance, optimization pressure on safety paths) demonstrates appropriate awareness of the repo's threat model.

Findings

Info

  • [style] PR body — The PR description notes "make lint could not run due to sandbox network restrictions." CLAUDE.md requires running make lint before submission. Since this is a documentation-only change (Markdown files), lint risk is negligible, and the post-script verification section confirms pre-commit hooks passed on the runner. Non-blocking.

Footer

Outcome: approve
This review applies to SHA 7bd494f8604fc065bab1cec8fce80988a25ce065. Any push to the PR head clears this review and requires a new evaluation.

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Experiment: optimise agent configurations, agentic team and workflow using evolutionary algorithms

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