Playbook: Agent SLOs, Incident Response & Lifecycle

Practical guidance to operate AI agents as reliable production services: learn how to set measurable SLOs, build acceptance tests, author incident runbooks, assign ownership, and run lifecycle checks so agents help people instead of surprising them.

Why this matters

Teams increasingly embed AI agents into workflows — routing customer requests, automating approvals, summarizing documents, or assisting on the factory floor. When those agents run without clear expectations, failures are slow to detect and slow to fix. This playbook teaches the operational disciplines that turn prototypes into dependable tools: define what success looks like, watch for when it breaks, and make it easy to restore service and learn from incidents.

What you'll understand and be able to do

After using this playbook you will be able to:

• Write concise, measurable SLOs for agent availability, correctness, and response time that match user needs.
• Create and run an acceptance test suite that catches common failure modes before deployment.
• Author an incident runbook with detection signals, triage steps, escalation paths, and rollback criteria.
• Assign clear owners and handoffs across development, operations, and business stakeholders.
• Schedule post-incident reviews and lifecycle activities (retraining, dependency updates, deprecation) to reduce repeat failures.

Practical examples

Examples show how the playbook applies across contexts:

• Small service company: define an SLO for intent-classification accuracy to avoid routing customers to the wrong team and create a one-click rollback for recent model updates.
• Healthcare support team: set strict availability and auditability expectations for an agent that pre-screens patient intake, and create a runbook that routes ambiguous cases to clinicians immediately.
• Manufacturing line: monitor agent-driven scheduling decisions for latency and constraint violations; include explicit safety checks and a manual override in the runbook.
• Research group: require an acceptance test that verifies data lineage and provenance before an agent is given write access to shared datasets.

What's included and how to use it

This resource bundles two practical items you can apply immediately: the Agent Acceptance Test Suite & SLO Checklist and the Agent Safety, Guardrails & Incident Runbook. Use the checklist to translate user expectations into measurable SLOs and tests. Use the runbook to document detection, triage, containment, and restoration steps. Together they form a minimum viable operational practice you can adopt and adapt to your environment.

How it fits in a broader AI operations journey

This playbook is a tactical step inside the broader domain of Applying Artificial Intelligence: it helps move teams from exploring "Can an agent do this?" to operating agents that reliably achieve real outcomes. If you are designing agents for your team, consider pairing this playbook with the Journey: Build AI Agents That Work for Your Team to cover design, prototyping, and monitoring end-to-end.