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affaan-m/agent-introspection-debugging

affaan-m

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.

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origin:ECC
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v1.2Saved Jul 14, 2026

Agent Introspection Debugging

Use this skill when an agent run is failing repeatedly, consuming tokens without progress, looping on the same tools, or drifting away from the intended task.

This is a workflow skill, not a hidden runtime. It teaches the agent to debug itself systematically before escalating to a human.

When to Activate

  • Maximum tool call / loop-limit failures
  • Repeated retries with no forward progress
  • Context growth or prompt drift that starts degrading output quality
  • File-system or environment state mismatch between expectation and reality
  • Tool failures that are likely recoverable with diagnosis and a smaller corrective action

Scope Boundaries

Activate this skill for:

  • capturing failure state before retrying blindly
  • diagnosing common agent-specific failure patterns
  • applying contained recovery actions
  • producing a structured human-readable debug report

Do not use this skill as the primary source for:

  • feature verification after code changes; use verification-loop
  • framework-specific debugging when a narrower ECC skill already exists
  • runtime promises the current harness cannot enforce automatically

Four-Phase Loop

Phase 1: Failure Capture

Before trying to recover, record the failure precisely.

Capture:

  • error type, message, and stack trace when available
  • last meaningful tool call sequence
  • what the agent was trying to do
  • current context pressure: repeated prompts, oversized pasted logs, duplicated plans, or runaway notes
  • current environment assumptions: cwd, branch, relevant service state, expected files

Minimum capture template:

## Failure Capture
- Session / task:
- Goal in progress:
- Error:
- Last successful step:
- Last failed tool / command:
- Repeated pattern seen:
- Environment assumptions to verify:

Phase 2: Root-Cause Diagnosis

Match the failure to a known pattern before changing anything.

Pattern Likely Cause Check
Maximum tool calls / repeated same command loop or no-exit observer path inspect the last N tool calls for repetition
Context overflow / degraded reasoning unbounded notes, repeated plans, oversized logs inspect recent context for duplication and low-signal bulk
ECONNREFUSED / timeout service unavailable or wrong port verify service health, URL, and port assumptions
429 / quota exhaustion retry storm or missing backoff count repeated calls and inspect retry spacing
file missing after write / stale diff race, wrong cwd, or branch drift re-check path, cwd, git status, and actual file existence
tests still failing after “fix” wrong hypothesis isolate the exact failing test and re-derive the bug

Diagnosis questions:

  • is this a logic failure, state failure, environment failure, or policy failure?
  • did the agent lose the real objective and start optimizing the wrong subtask?
  • is the failure deterministic or transient?
  • what is the smallest reversible action that would validate the diagnosis?

Phase 3: Contained Recovery

Recover with the smallest action that changes the diagnosis surface.

Safe recovery actions:

  • stop repeated retries and restate the hypothesis
  • trim low-signal context and keep only the active goal, blockers, and evidence
  • re-check the actual filesystem / branch / process state
  • narrow the task to one failing command, one file, or one test
  • switch from speculative reasoning to direct observation
  • escalate to a human when the failure is high-risk or externally blocked

Do not claim unsupported auto-healing actions like “reset agent state” or “update harness config” unless you are actually doing them through real tools in the current environment.

Contained recovery checklist:

## Recovery Action
- Diagnosis chosen:
- Smallest action taken:
- Why this is safe:
- What evidence would prove the fix worked:

Phase 4: Introspection Report

End with a report that makes the recovery legible to the next agent or human.

## Agent Self-Debug Report
- Session / task:
- Failure:
- Root cause:
- Recovery action:
- Result: success | partial | blocked
- Token / time burn risk:
- Follow-up needed:
- Preventive change to encode later:

Recovery Heuristics

Prefer these interventions in order:

  1. Restate the real objective in one sentence.
  2. Verify the world state instead of trusting memory.
  3. Shrink the failing scope.
  4. Run one discriminating check.
  5. Only then retry.

Bad pattern:

  • retrying the same action three times with slightly different wording

Good pattern:

  • capture failure
  • classify the pattern
  • run one direct check
  • change the plan only if the check supports it

Integration with ECC

  • Use verification-loop after recovery if code was changed.
  • Use continuous-learning-v2 when the failure pattern is worth turning into an instinct or later skill.
  • Use council when the issue is not technical failure but decision ambiguity.
  • Use workspace-surface-audit if the failure came from conflicting local state or repo drift.

Output Standard

When this skill is active, do not end with “I fixed it” alone.

Always provide:

  • the failure pattern
  • the root-cause hypothesis
  • the recovery action
  • the evidence that the situation is now better or still blocked
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Overall Score

88/100

Grade

A

Excellent

Safety

92

Quality

88

Clarity

89

Completeness

82

Summary

This skill provides a four-phase structured debugging workflow for AI agents that are looping, consuming tokens without progress, or failing repeatedly. It teaches agents to systematically capture failure state, diagnose root causes against known patterns, apply contained recovery actions, and produce human-readable introspection reports before escalating to humans.

Detected Capabilities

self-diagnosislog analysisstate inspectionstructured reportingrecovery planning

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

debug infinite loopagent stuck retryingcontext overflowfix agent failurediagnose token burnintrospection report

Use Cases

  • Debug agent infinite loops or repeated tool calls
  • Diagnose context overflow or prompt drift issues
  • Verify and recover from file-system or environment state mismatches
  • Isolate transient vs. deterministic failures before retry
  • Generate structured debug reports for human handoff
  • Recover from quota exhaustion or service availability failures

Quality Notes

  • Excellent scope boundaries: skill is explicitly scoped to self-debugging workflow, not primary feature verification or runtime configuration changes
  • Strong use of failure patterns table with diagnostic checks — teaches agent to match symptoms to root causes systematically
  • Clear phase structure with templates makes the workflow repeatable and legible to both agents and humans
  • Heuristic priority ordering (restate, verify, shrink, check, retry) provides clear decision tree to prevent common bad patterns
  • Well-integrated with other ECC skills through explicit cross-references (verification-loop, continuous-learning-v2, council, workspace-surface-audit)
  • Output standard ensures skill doesn't end with vague claims — always requires pattern, hypothesis, action, and evidence
  • All instructions are grounded in reversible, observable actions — no speculative claims like 'reset agent state'
  • Captures both logic failures and environmental failures, avoiding single-point-of-failure thinking
Model: claude-haiku-4-5-20251001Analyzed: Jul 14, 2026

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Version History

v1.2

Content updated

2026-07-14

Latest
v1.1

Content updated

2026-04-20

v1.0

No changelog

2026-04-12

Use affaan-m/agent-introspection-debugging in your dev environment

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