Playbook Scope
This playbook defines how to run AI agents in production with reliability, oversight, and measurable business value.
- Design agent workflows with clear boundaries and fallback logic.
- Control cost and latency without degrading response quality.
- Embed human escalation for high-risk or low-confidence outcomes.
Reference Architecture
- Intent layer: classify request type, user identity, and risk level.
- Planning layer: break tasks into verifiable sub-steps before tool calls.
- Execution layer: run tool actions with timeout, retry, and budget controls.
- Assurance layer: policy checks, human handoff, and audit logging.
Reliability and SLOs
Define production targets before broad rollout.
- Task success rate threshold by workflow type.
- 95th percentile response time and tool timeout policy.
- Escalation rate and resolution time for human handoff cases.
- Cost per successful completion by channel.
Incident and Recovery Runbook
- Detect: monitor policy violations, tool failures, and abnormal output patterns.
- Contain: trigger safe-mode behavior and route critical flows to human ops.
- Recover: patch prompts/workflows, revalidate, and re-enable with staged traffic.
FAQ
- What is the first production guardrail to implement?
Implement confidence-aware fallback and human escalation first. It prevents most high-impact failures while workflows mature.
- How do we control agent cost?
Use token budgets, capped retries, and routing rules that send low-complexity tasks to lightweight workflows.
- Do we need a separate agent platform team?
Not initially. Start with shared ownership, then create a dedicated team once agent usage spans multiple critical workflows.