Strategy · 4 minute read
AI Agent Runbook Template for Operations Teams
An AI agent runbook gives the on-call engineer what they need to operate an agent safely: a system overview with dependencies, health checks and dashboards, the agent-specific failure modes with diagnosis and response, the kill switch and rollback procedures, escalation contacts including the process owner, safe change procedures, and the evidence to capture for a postmortem.
An agent that has been specified, evaluated, and launched is a production service, and the person on call at three in the morning needs what they would need for any service: a runbook. The difference is the failure modes. Agents degrade in quality without throwing errors, loop through steps until a budget stops them, change behavior when a model version changes, and act on content that told them to. This template covers those, alongside the standard material, and it is the document FISTA hands over with every agent. It complements the AI observability whitepaper and the incident process in the AI incident response checklist.
What does the template contain?
| Section | Content |
|---|---|
| 1. Overview | Purpose, owner, autonomy level, registry identifier, model card reference |
| 2. Architecture and dependencies | Gateway routes, models, tools and their systems, retrieval indexes, approval queue |
| 3. Health checks and dashboards | Signals, thresholds, where to look |
| 4. Failure modes | Signal, diagnosis, bounded response, escalation, for each |
| 5. Kill switch and rollback | Procedures, authority, restoration |
| 6. Escalation | Engineering, platform, security, process owner, with conditions |
| 7. Safe changes | What may change in an incident and how; what must wait for the gate |
| 8. Evidence capture | What to preserve for the postmortem |
| 9. Scheduled operations | Sampling reviews, evaluation runs, access reviews, deprecation dates |
| 10. Contacts and history | On-call rotation; incident log |
What are the agent-specific failure modes?
| Failure | Signal | Diagnosis | Bounded response |
|---|---|---|---|
| Quality drift | Sampled scores falling; override rate rising | Input mix change, stale content, model change | Reduce autonomy to suggest; check recent changes; route to fallback model if a model change is implicated |
| Runaway steps | Step or token budget alerts; cost spike | Spec gap, tool returning unexpected results, loop | Enforce budgets; disable the implicated tool; escalate to owner |
| Injected content | Unexpected actions; plan changes after reads | Untrusted content in instruction position | Disable consequential tools; preserve trace; escalate to security |
| Tool failure | Error rate per tool rising | Upstream outage, permission change, API change | Disable tool; agent escalates cases; notify system owner |
| Model or prompt regression | Quality drop coinciding with a release | Regression gate bypassed or insufficient | Roll back the release; re-run the suite |
| Approval queue backlog | Queue depth; expired approvals | Approver capacity | Notify approvers; reduce intake; never auto-approve |
| Retrieval degradation | Groundedness scores falling | Index staleness, embedding change | Re-index; validate retrieval golden set |
Each entry names the dashboard panel, the exact response steps, and who to escalate to.
How do the kill switch and rollback work?
The kill switch disables the agent's identity at the gateway and revokes brokered credentials; in-flight actions stop; new requests route to the previous process or a holding queue. It is exercised on a schedule and its last test date is recorded. Rollback restores the previous release of prompts, routing, and configuration through the gateway's versioned control plane. Both procedures name who may invoke them. Design guidance is in AI agent kill switch design.
Who is escalated, and when?
Engineering for availability and tool failures; the platform team for gateway and routing; security for suspected injection or permission issues; and the process owner for quality and policy decisions, because whether to keep the agent running at reduced autonomy or stop it is their call. The runbook gives conditions for each.
What changes are safe during an incident?
Configuration with immediate rollback: routing to a tested fallback model, tightening cascade thresholds, reducing autonomy, disabling a tool, pausing intake. Prompt and specification edits are not, and the runbook says so plainly, because the edit that "fixes" one case at three in the morning is the regression discovered at nine. The gate discipline is in AI regression testing.
What evidence is captured?
Traces for affected tasks, the versions in effect, dashboards at the time, actions taken and by whom, and affected records or customers. This feeds the AI incident postmortem template.
What runs on a schedule?
Weekly sampling reviews with the process owner; evaluation runs before every release; monthly access reviews of the agent's permissions; quarterly performance reviews; deprecation dates for models and libraries with migration milestones; kill-switch tests.
What are the common mistakes?
- A runbook copied from a service with no agent failure modes.
- Kill switch untested.
- Process owner missing from escalation.
- Prompt edits during incidents.
- No evidence capture, so the postmortem is guesswork.
How does FISTA Solutions help?
FISTA Solutions hands over this runbook, tested, with every AI agent it delivers, trains the on-call team through forward deployed engineers, and installs the operating practice across the fleet through AI enablement. FISTA has delivered 150+ projects for 50+ companies across 12+ countries with 99.9% uptime.
To operationalize an agent that shipped without one, message FISTA on WhatsApp, or read how to monitor AI in production for the signals the runbook depends on.
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Straightforward guidance for evaluating scope, fit, and the next step.
01What makes an AI agent runbook different from a service runbook?
It covers failure modes services do not have: quality drift without errors, runaway step counts, behavior changes from model or prompt updates, injected content redirecting actions, and tool permission failures. It also includes the process owner in escalation, because many agent incidents are quality or policy decisions rather than outages.
02What should the health checks include?
Availability and latency of the gateway, models, tools, and retrieval; error rates per tool; step and token counts per task against budgets; escalation and override rates; sampled quality scores; cost per task; and queue depth for approvals. Dashboards show these with alert thresholds the runbook references.
03How should the kill switch work?
A single procedure that disables the agent's identity at the gateway and, where applicable, revokes its credentials, so in-flight actions stop and new ones cannot start, with work routed back to the previous process. It is tested on a schedule, and the runbook records who may use it and how to restore service afterward.
04What changes are allowed during an incident?
Configuration changes with immediate rollback such as routing to a fallback model, tightening a cascade threshold, reducing autonomy to suggest, or disabling a tool, all logged. Prompt and specification changes still go through the regression gate; the runbook says so to stop well-meant edits under pressure from becoming the next incident.
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