Measuring AGENTS.md effectiveness: Configuration Effectiveness (CE) as a feedback loop

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Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
35/100
Tipo di issue
Funzionalità
Chiarezza
Da chiarire
Stato di attività
Attiva
Ambito
ai, documentation

Direzione di ricerca

Start by reviewing the CE implementation in agent-skills PR #52, then read the Verification proposal (#189) and multi-agent context proposal (#185). The issue does not identify a specific file or spec edit; progress would require agreeing which CE findings should become AGENTS.md guidance and documenting the resulting recommendations.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Summary

I've built a measurement tool (rp-why CE) that empirically measures how well an AGENTS.md configuration is being followed by an AI agent. This creates a feedback loop that's currently missing from the ecosystem: practitioners write AGENTS.md files but have no data on whether those instructions actually work.

Implementation: https://github.com/block/agent-skills/pull/52

What CE Measures

For each measurable instruction in an AGENTS.md file:

  • Implicit command adherence - when a trigger phrase is used, does the agent execute the expected action?
  • Dead instruction detection - which instructions have never been triggered across N sessions?
  • Token waste - what percentage of configuration tokens are spent on instructions that never fire?
  • Override frequency - how often does the practitioner correct the agent on something the AGENTS.md should have handled?

Why This Matters for the AGENTS.md Spec

  1. Validates spec design decisions. CE data shows which AGENTS.md patterns agents reliably follow. This can inform spec recommendations (e.g., 'tables are more reliably parsed than prose for behavioral instructions').

  2. Supports the Verification section proposal (#189). CE can measure whether verification steps declared in AGENTS.md are actually executed. This provides empirical evidence for whether a standard Verification section improves agent behavior.

  3. Informs the multi-agent context discussion (#185). CE's dead instruction detection reveals when instructions are irrelevant to the current agent - supporting the case for agent-specific overlays.

  4. Provides data on configuration size limits. CE's token waste metric shows when configurations have grown beyond what agents can reliably hold. This could inform spec guidance on recommended configuration length.

Key Finding

Running CE against 20 real Goose sessions with a 13-instruction implicit commands table:

  • 9 of 13 commands are dead (never triggered in the measurement window)
  • 74.9% of configuration tokens are wasted on untriggered instructions
  • Moving situational commands to project-level AGENTS.md files would reduce waste significantly

This suggests the spec should recommend lean global configurations with situational instructions at the project or subdirectory level - which aligns with the existing nested AGENTS.md guidance.

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