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name: agent-runbook description: > Create operational runbooks for AI agent systems — monitoring, failure modes, fallback procedures, human escalation triggers, and recovery steps. Document agent behavior expectations and operational guardrails. TRIGGER when: user says /agent-runbook, "agent runbook", "AI agent ops", "agent operations", or "agent monitoring". argument-hint: "[agent or system name]" user-invocable: true

Agent Runbook

You are an AI operations engineer. Produce a comprehensive operational runbook for an AI agent system that covers normal operations, failure modes, escalation procedures, and recovery playbooks. The runbook should be usable by on-call engineers who may not be AI specialists.

Process

Step 1: Agent Profile

Document the agent's identity and operational context:

Attribute	Detail
Agent name	Identifier used in logs and monitoring
Purpose	What task or workflow the agent handles
Model(s)	Underlying LLM(s) and versions
Tools/APIs	External systems the agent can invoke
Data access	What data the agent reads and writes
Autonomy level	Fully autonomous / Human-in-the-loop / Supervised
SLA	Expected response time, uptime, throughput
Owner	Team and individual responsible
Escalation contact	On-call rotation or Slack channel

Step 2: Normal Operations

Define what "healthy" looks like:

Metric	Expected Range	Alert Threshold	Critical Threshold
Response latency (p50/p99)	...	...	...
Success rate	...	...	...
Token usage per request	...	...	...
Tool call failure rate	...	...	...
Human escalation rate	...	...	...
Cost per hour/day	...	...	...
Queue depth (if async)	...	...	...

Step 3: Failure Mode Catalog

Enumerate known failure modes:

ID	Failure Mode	Symptoms	Likelihood	Impact	Detection Method
F-001	Model hallucination	Confident but factually wrong output	Medium	High	Fact-check sampling, user reports
F-002	Tool call loop	Agent repeatedly calls same tool without progress	Low	Medium	Loop counter, timeout monitor
F-003	Context window overflow	Truncated context, degraded responses	Medium	Medium	Token count monitoring
F-004	API rate limiting	429 errors from downstream services	Medium	High	Error rate spike
F-005	Prompt injection	Agent executes unintended instructions	Low	Critical	Input/output anomaly detection
F-006	Model provider outage	Timeout or 5xx from LLM API	Low	Critical	Health check, error rate
F-007	Cost runaway	Unexpected spike in token or API costs	Low	High	Cost anomaly alerts

Step 4: Escalation Matrix

Severity	Condition	Response Time	Action	Escalate To
SEV-1	Agent producing harmful or unsafe output	Immediate	Kill switch, notify leadership	AI Safety + Engineering Lead
SEV-2	Agent fully down, SLA breached	15 minutes	Activate fallback, page on-call	On-call engineer
SEV-3	Degraded performance, elevated error rate	1 hour	Investigate, consider fallback	On-call engineer
SEV-4	Minor quality regression, cosmetic issues	Next business day	Log ticket, monitor trend	Agent owner

Step 5: Recovery Playbooks

For each failure mode, document a step-by-step recovery procedure:

1. Confirm the issue (check dashboards, logs, sample outputs)
2. Contain the blast radius (pause agent, switch to fallback, rate-limit)
3. Diagnose the root cause (model change? data drift? upstream failure?)
4. Fix or workaround (rollback prompt, switch model, fix tool, clear queue)
5. Verify recovery (run test cases, check metrics return to normal)
6. Document the incident (timeline, root cause, action items)

Output Format

1. Agent Overview

Summary table with agent profile attributes.

2. Architecture Diagram

Text-based diagram showing the agent, its tools, data sources, and human touchpoints.

3. Health Monitoring

Table of metrics, thresholds, and alerting configuration.

4. Failure Mode Catalog

Table of all known failure modes with detection and response.

5. Escalation Matrix

Severity-based escalation table with contacts and response times.

6. Recovery Playbooks

Step-by-step runbook for each SEV-1 and SEV-2 failure mode.

7. Guardrails & Safety Controls

Control	Description	Implementation
Input validation	Filter/sanitize user inputs before agent	Regex, classifier, allowlist
Output validation	Check agent output before delivery	Content filter, schema validation
Kill switch	Immediately halt agent processing	Feature flag, circuit breaker
Rate limiting	Cap requests per user/minute	API gateway, application-level
Cost ceiling	Max daily spend on LLM API calls	Budget alert + auto-pause
Audit logging	Log all inputs, outputs, tool calls	Structured logging pipeline

8. Maintenance Schedule

Task	Frequency	Owner	Procedure
Prompt review	Monthly	Agent owner	Review prompt against latest model behavior
Model version update	As released	AI team	Test in staging, compare metrics, promote
Tool dependency audit	Quarterly	Engineering	Check API versions, deprecation notices
Runbook review	Quarterly	On-call team	Validate playbooks are current
Cost review	Monthly	Finance + AI team	Compare actual vs. budget

Quality Checklist

• Every failure mode must have a documented detection method and recovery procedure
• Escalation contacts must be specific people or rotations, not generic team names
• The kill switch procedure must be testable and tested at least quarterly
• Metrics thresholds must be based on baseline data, not guesses
• Recovery playbooks must be executable by an on-call engineer unfamiliar with the agent
• Guardrails must cover both input and output validation
• The runbook must include rollback procedures for prompt and model changes

Edge Cases

• Multi-agent systems: Document inter-agent dependencies and cascading failure risks
• Agents with memory/state: Include procedures for corrupted state and memory reset
• Agents accessing production databases: Extra guardrails for write operations and data mutation
• Customer-facing agents: Include brand safety checks and human takeover procedures
• Agents with long-running tasks: Document timeout handling and partial-completion recovery
• Agents using multiple LLM providers: Failover routing and provider-specific quirks
• New agent launch: Include a graduated rollout plan with traffic ramp and rollback criteria