agent-loop-learning-boundary

Imported from voku/agent-loop (resources/skills/agent-loop-learning-boundary/SKILL.md). Install upstream with npx skills add voku/agent-loop --skill agent-loop-learning-boundary. Copyright stays with the author.

Agent Loop Learning Boundary

Trigger Anchor: Post-implementation / pre-close learning disposition -> log truthful Recall outcomes, validate Learning root, record Run learning decision (findings_recorded, no_durable_learning, or follow_up_required), never treat raw findings as approved guidance.

Core Boundary

Finding (raw observation)
  -> Proposal (structured candidate)
  -> Reviewed Decision (named human/governed authority)
  -> Durable Guidance (active rules / constraints)

Findings are not durable memory. A Run learning close-out records what happened; it does not approve future rules or alter guidance without a reviewed decision.

Learning Disposition Table

--finding and --follow-up already state the decision. --reason is optional context: do not write a sentence to say nothing durable was learned.

Fast Path Sequence

1. Recall Close-out: Log the compiled draft so Recall records the selected guidance as machine evidence. Add outcome rows only for guidance you actually judged; an untouched sparse draft is valid when nothing notable happened. A helpful row needs attribution: seen_before_decision is false when you first opened the guidance while filling the draft, and also_prescribed_by lists the task prompt, Contract, skill, template, Constraint, or repository docs that already prescribed the same decision ([] only if nothing else did). Honest false/non-empty answers are valid evidence; do not tune them toward attribution. If finish reports learning_maintenance, a relevant LearningNote was withheld for evidence drift in files you touched: review it with the named command and republish or retire it only through the Learning owner.

   vendor/bin/agent-loop recall log-outcome \
     --draft <recall-root>/<task-id>/recall-log.draft.json \
     --by <actor> \
     --commit <sha>

2. Validate Learning Root:

   vendor/bin/agent-loop learn validate

3. Record Governed Decision:

   vendor/bin/agent-loop workflow learn <task-id> \
     --status no_durable_learning \
     --by <actor>

Bad vs Good Learning Disposition

Bad

Self-approving a finding into durable guidance to bypass human review:

# Pretending a raw task observation is instantly an approved rule
vendor/bin/agent-loop workflow learn TASK-1 --status findings_recorded --by self --reason "I learned this so it is now a rule"

Good

Separating task close-out from durable promotion:

# Record the finding in the task Run, validate candidate, await human review
vendor/bin/agent-loop workflow learn TASK-1 \
  --status findings_recorded \
  --finding finding.TASK-1.001 \
  --by model-agent \
  --reason "Discovered that Docker container names require explicit prefixing on host."

Guidance & Memory Commands

• Evaluate Drift: vendor/bin/agent-loop learn guidance-evaluate (read-only audit of existing guidance).
• Memory Queue: vendor/bin/agent-loop memory review --file=MEMORY.md (inspect candidates without modifying durable memory).
• Proposal Validation: vendor/bin/agent-loop learn proposal-validate --proposal <path> (structural schema check).

Validation Check

Before workflow close --status done:

• Recall selection events are logged for the compilation; guidance outcomes exist only for items actually judged.
• vendor/bin/agent-loop learn validate exits with code 0.
• Exactly one truthful Run learning decision is recorded.
• No synthetic human approvals or unreviewed promotions were fabricated.