agent-observability-fault-tolerance: Security & Code Review for AI... | OpenAgentSkill

Overview

Design and review agent runtime observability and model/provider fault tolerance. Use when adding metrics, traces, cost tracking, audit alignment, structured runtime events, model fallback, structured output repair, refusal handling, provider timeouts, or local execution tree diagnostics.

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Agent Observability and Fault Tolerance

Skill Interface

Name: agent-observability-fault-tolerance.
Description: Design and review agent runtime observability and model or provider fault tolerance for metrics, traces, cost tracking, audit alignment, runtime events, fallback, repair, refusal handling, timeouts, and execution tree diagnostics.
Parameters: Task and step lifecycle, model and tool calls, retry and fallback policy, trace identifiers, metric names, cost fields, redaction policy, terminal states, and verification scenarios.
Instructions: Use this skill when runtime behavior must be observable across success, retry, fallback, refusal, timeout, and terminal paths. Add bounded metrics, preserve trace continuity, classify failures, redact sensitive data, and test telemetry for each recovery path.

An agent runtime should make work visible across task, step, model, tool, retry, and terminal paths. Fault tolerance should be explicit and observable.

Signals

Capture metrics and traces across:

Task lifecycle.
Step lifecycle.
Tool calls.
Model calls.
Retry attempts.
Structured output repair.
Fallback provider selection.
Human approval or refusal.
Compensation.
Terminal state.

Metrics

Track:

Task success rate.
Task completion latency.
Step failure rate.
Retry recovery rate.
Resume success rate.
Idempotency hit rate.
Duplicate side-effect prevention rate.
Tool success, timeout, and latency.
Permission denial rate.
Compensation success rate.
Model fallback rate.
Structured output repair success rate.
Cost per successful task.

Trace Shape

Use a trace hierarchy that follows the actual execution chain:

request
  agent task
    graph node
      model call
      tool call
      retry attempt
      repair attempt

Include stable identifiers when available: request id, task id, step id, run id, thread id, graph id, node name, tool call id, provider, model category, duration, retry count, terminal status, and error code.

Fault Tolerance

Provider temporary failure: retry within budget, then fallback if policy allows.
Provider timeout: retry with backoff, then fallback or return timeout result.
Structured output parse error: attempt bounded repair.
Structured output validation error: repair with a precise validation hint.
Refusal or policy denial: do not retry blindly; return a structured refusal signal.
Tool timeout or upstream failure: classify and apply retry policy.

Safety Rules

Do not log credentials, raw authorization headers, private payloads, full prompts, full conversations, or unredacted personal data.
Do not hide fallback, repair, retry, or degraded execution from telemetry.
Do not conflate provider failure, validation failure, refusal, and timeout into one generic error.

Verification

Test provider outage, timeout, structured parse failure, validation failure, refusal, retry exhaustion, fallback success, cost accounting, trace continuity, metric emission, and redaction.

View source ↗

File metadata

name: agent-observability-fault-tolerance
description: Design and review agent runtime observability and model/provider fault tolerance. Use when adding metrics, traces, cost tracking, audit alignment, structured runtime events, model fallback, structured output repair, refusal handling, provider timeouts, or local execution tree diagnostics.

View original text

---
name: agent-observability-fault-tolerance
description: Design and review agent runtime observability and model/provider fault tolerance. Use when adding metrics, traces, cost tracking, audit alignment, structured runtime events, model fallback, structured output repair, refusal handling, provider timeouts, or local execution tree diagnostics.
---

# Agent Observability and Fault Tolerance

## Skill Interface

- Name: agent-observability-fault-tolerance.
- Description: Design and review agent runtime observability and model or provider fault tolerance for metrics, traces, cost tracking, audit alignment, runtime events, fallback, repair, refusal handling, timeouts, and execution tree diagnostics.
- Parameters: Task and step lifecycle, model and tool calls, retry and fallback policy, trace identifiers, metric names, cost fields, redaction policy, terminal states, and verification scenarios.
- Instructions: Use this skill when runtime behavior must be observable across success, retry, fallback, refusal, timeout, and terminal paths. Add bounded metrics, preserve trace continuity, classify failures, redact sensitive data, and test telemetry for each recovery path.

An agent runtime should make work visible across task, step, model, tool,
retry, and terminal paths. Fault tolerance should be explicit and observable.

## Signals

Capture metrics and traces across:

- Task lifecycle.
- Step lifecycle.
- Tool calls.
- Model calls.
- Retry attempts.
- Structured output repair.
- Fallback provider selection.
- Human approval or refusal.
- Compensation.
- Terminal state.

## Metrics

Track:

- Task success rate.
- Task completion latency.
- Step failure rate.
- Retry recovery rate.
- Resume success rate.
- Idempotency hit rate.
- Duplicate side-effect prevention rate.
- Tool success, timeout, and latency.
- Permission denial rate.
- Compensation success rate.
- Model fallback rate.
- Structured output repair success rate.
- Cost per successful task.

## Trace Shape

Use a trace hierarchy that follows the actual execution chain:

```text
request
agent task
graph node
model call
tool call
retry attempt
repair attempt
```

Include stable identifiers when available: request id, task id, step id, run
id, thread id, graph id, node name, tool call id, provider, model category,
duration, retry count, terminal status, and error code.

## Fault Tolerance

- Provider temporary failure: retry within budget, then fallback if policy
allows.
- Provider timeout: retry with backoff, then fallback or return timeout result.
- Structured output parse error: attempt bounded repair.
- Structured output validation error: repair with a precise validation hint.
- Refusal or policy denial: do not retry blindly; return a structured refusal
signal.
- Tool timeout or upstream failure: classify and apply retry policy.

## Safety Rules

- Do not log credentials, raw authorization headers, private payloads, full
prompts, full conversations, or unredacted personal data.
- Do not hide fallback, repair, retry, or degraded execution from telemetry.
- Do not conflate provider failure, validation failure, refusal, and timeout
into one generic error.

## Verification

Test provider outage, timeout, structured parse failure, validation failure,
refusal, retry exhaustion, fallback success, cost accounting, trace continuity,
metric emission, and redaction.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 28 GitHub stars
Stars/forks activity: 28 stars, 0 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: HsienW/ai-agent-engineering-playbook
License: MIT
Version: Unknown
Last GitHub push: Sep 6, 2026
Registry updated: Sep 12, 2026
Instruction path: skills-runtime-governance/agent-observability-fault-tolerance/SKILL.md @ 957a8bcdc457

Version reported in registry metadata; check source releases before relying on it.

Quality

56/100

Promising

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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}

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Creator: HsienW
Source: HsienW/ai-agent-engineering-playbook
Indexed by: OpenAgentSkill community index

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