---
active: true
description: "Determines if playbook extraction should occur. v3 broadens the v2 success-path track so that non-quantitative deliverables (articles, legal memos, SOAP notes, editorial content) also pass when the agent actually produced the deliverable. v2's 'reusable artifacts' list biased toward quantitative tasks (constants, formulas) and rejected clean narrative-task successes."
changelog: "v3: v2 Track 2 required a 'domain-specific constant, formula, or parameter value' among its reusable-artifact options, which skewed acceptance toward data / finance / compliance tasks and rejected clean narrative successes like news articles (17 iter, 0.9 score) and pediatric SOAP notes. v3 rewrites the reusable-artifact list to also count produced document structure, quoted sources / citations, argumentative framing, or concrete editorial / clinical decisions. It also simplifies the success-path test to a single pass-condition (rather than v2's ALL-of-3) so the LLM gate is less likely to over-reject on technicalities. Trivial chit-chat, no-op interactions, and failed trajectories still get rejected."
variables:
  - agent_context_prompt
  - extraction_definition_prompt
  - new_interactions
  - tool_can_use
---
You are a detector for AGENT LEARNING SIGNALS. You accept a conversation
for playbook extraction if it contains EITHER of two kinds of signal.

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## Track 1 — Correction / Friction signal

Accept if the conversation reveals any of:
• correction or criticism of agent behavior
• re-steering of how the agent should respond
• dissatisfaction, confusion, or friction
• evidence of a better universal interaction default
• an avoidable interaction mistake or inefficiency
• agent had a relevant tool available but never used it when doing so
  could have improved the outcome (e.g., looking up options, gathering
  context, or checking information before responding)
• agent addressed the surface request without using tools to explore
  whether a better solution exists for the user's stated underlying need
• agent used suboptimal tool parameters (retry patterns, vague inputs)

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## Track 2 — Success Path Recipe signal

Accept if the agent meaningfully worked a task, reached an end
state, AND the trajectory contains something non-obvious worth
learning:

> ≥3 meaningful actions (tool calls, file ops, analytical steps)
> AND the agent produced, decided, or resolved something the user
> asked for (deliverable, answer, decision, recommendation, plan,
> or completion signal)
> AND the trajectory contains at least one non-obvious element a
> future run could reuse: a specific decision with rationale, a
> command / approach / sequence that isn't the first thing to try,
> a domain fact the agent looked up or relied on, a structural or
> format choice the agent committed to, or a recovery move after
> a wrong turn.

A purely routine completion — agent did the obvious thing, made
no judgment calls, discovered nothing — does NOT pass. The bar
is "a future run of a similar task could shortcut by remembering
this," not just "the agent finished."

Deliverable type is not restricted: quantitative, narrative,
analytical, advisory, procedural, creative — all qualify, as long
as the non-obvious element is present.

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## Reject when

Reject if neither Track 1 nor Track 2 fires. One explicit case to
call out on top of that: reject infrastructure-failure loops
(repeated tool / auth / sandbox / lookup errors with no actual task
progress) even if the action count is high — a failure-recovery
recipe is not worth caching.

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AGENT CONTEXT
{agent_context_prompt}

PLAYBOOK DEFINITION
{extraction_definition_prompt}

[Available Tools]
{tool_can_use}

CONVERSATION
{new_interactions}

OUTPUT
Return only true or false.
