Agent Introspection Debugging

affaan-m/ECC/pi/core/skills/agent-introspection-debugging

作者 affaan-mef648e01899ba3e8dc6371642deaaf64b4477775無授權條款275K 個星標收錄於 2026年10月9日更新於 2026年10月9日儲存庫4 天前更新

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

僅含說明AI & Agents
AI 產生的概覽

一套四階段工作流程,用於診斷並復原 AI 代理程式執行失敗,而不是盲目重試。

功能
引導代理程式完成四階段循環:擷取失敗狀態、將其對應到已知失敗模式、執行最小的受控復原動作,並撰寫結構化的自省報告。它提供擷取範本、診斷模式表、復原啟發式規則和輸出標準。產出為書面除錯報告,而非程式碼或檔案。
適用情境
適用於代理程式執行反覆失敗、在同一工具上迴圈、耗盡工具呼叫上限、消耗權杖卻無進展,或偏離既定任務的情況。它面向可能透過診斷復原的代理程式特有故障,而非功能驗證或特定框架的除錯。
執行需求
無需指令碼或工具,僅為純指令型工作流程技能。它引用其他 ECC 技能(如 verification-loop、continuous-learning-v2、council 和 workspace-surface-audit)作為後續步驟。

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:

markdown
## 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.

PatternLikely CauseCheck
Maximum tool calls / repeated same commandloop or no-exit observer pathinspect the last N tool calls for repetition
Context overflow / degraded reasoningunbounded notes, repeated plans, oversized logsinspect recent context for duplication and low-signal bulk
ECONNREFUSED / timeoutservice unavailable or wrong portverify service health, URL, and port assumptions
429 / quota exhaustionretry storm or missing backoffcount repeated calls and inspect retry spacing
file missing after write / stale diffrace, wrong cwd, or branch driftre-check path, cwd, git status, and actual file existence
tests still failing after “fix”wrong hypothesisisolate 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:

markdown
## 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.

markdown
## 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

來源與署名

來源:affaan-m/ECC位於pi/core/skills/agent-introspection-debugging提交ef648e0

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