os-improvement-loop

name	os-improvement-loop
plugin	agent-agentic-os
version	0.5.0
description	Pattern 5: Concurrent Event-Driven Multi-Agent Loop. Coordinates multiple Claude sessions as OS threads sharing a common event bus and memory address space. Every loop cycle is a full improvement cycle: execute, eval against benchmark (KEEP/DISCARD), emit friction events, and close with surveys, metrics, memory persistence, and Triple-Loop triggers.
allowed-tools	Read, Write, Edit, Bash, Glob, Grep

Concurrent Agent Loop

Treats concurrent sessions as OS threads sharing a common event bus and memory address space. Every cycle includes execution, independent peer evaluation, friction tracking, self-assessment surveys, post-run metrics, and memory persistence.

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When to Use This Pattern

Use when:

• Coordinating continuous improvement across multiple concurrent agent sessions.
• Evaluating and improving multiple skills, workflows, or templates in parallel.
• You need every cycle to generate measurable accuracy gains and persistent memory.

Do NOT use for:

• Single-session procedural tasks (use os-eval-runner directly).
• Signal-only coordination with no evaluation, survey, or memory steps.

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Key Invariants

• No-Rollback Rule: Never manually roll back changes during a cycle unless evaluate.py registers an explicit accuracy regression.
• Eval Gate Mandatory: Every modification must pass the independent evaluation gate (evaluate.py exit code 0). No manual bypasses.
• NEVER STOP Discipline: Do not abort a running loop due to minor/moderate errors. Complete the loop close checklist and log unresolved issues as Map Debt.
• Outer Loop Ownership: The outer loop owns session lifecycle. Inner loop tasks (os-eval-runner) must not prematurely close a session without running Stage 4 (memory promotion and survey collection).

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Stage Pointers & Reference Protocols

The execution details are split across modular references:

• Stage 0: Setup and Orientation — Pre-flight reads, registry, and packet design.
• Stage 1: INNER_AGENT Execution — Strategy execution, friction logging, and local scoring.
• Stage 2: PEER_AGENT Verification — Independent evaluation run and verdict formulation.
• Stage 3: Decision Logic — KEEP/DISCARD actions and correction packets.
• Stage 4: Loop Close Checklist — Surveys, ledger updates, memory promotion, and retrospectives.
• Orchestrator Meta-Survey — Meta-evaluation of loop coordination patterns.
• Lab Limits — Hard caps on iterations, eval sets, and critic invocations per run.

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Smoke Test

1. Verify Event Registry: Run os-init or start a test loop. Assert that context/events.jsonl registers start events correctly.
2. Execute Scorer: Run python3 ./scripts/evaluate.py --skill skills/todo-check/ on a dummy check to verify that exit codes map correctly (0 for KEEP, 1 for DISCARD, 2 for path error).
3. Friction Event Test: Propose a manual edit, emit a mock friction event, resolve it with friction.resolved, and verify the metrics engine logs the resolution gate pass.

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Gotchas

• Conflation of Loops: Conflating the inner target skill loop with the outer OS-improvement loop. Outer loop changes the OS workflows; inner loop changes target skills.
• Orphaned Sessions: Completing inner loop tasks but failing to run memory promotion and survey curation. Ephemeral findings are lost.
• Directory Symlinks: Creating directory-level symlinks from skills to shared roots. This violates ADR-003. Use file-level symlinks.

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HANDOFF_BLOCK Template

Every loop execution that completes a cycle must output this block in its handoff:

## HANDOFF_BLOCK
- **Cycle ID**: cycle-YYYYMMDD-HHMMSS
- **Target Skill**: [path/to/target]
- **Verdict**: KEEP / DISCARD
- **Score (Before -> After)**: [0.XX -> 0.YY]
- **Friction Events**: [N encountered / N resolved]
- **Outstanding Map Debt**: [list links or IDs]
- **Recommended Next Step**: [next hypothesis to test]