Technical Reference | AI Engineering Playbooks

A practical incident runbook for AI agent systems, covering common failure modes and response actions that reduce production impact. Most agent incidents are predictable: tool misuse, context drift, and weak guardrails. Build a failure taxonomy and link each class to detection and recovery playbooks. Track MTTR and recurrence to continuously harden your agent platform. Agent systems do not fail in one way. They fail across planning, context, tool invocation, and execution boundaries. Without a clear runbook, teams lose time arguing about symptoms instead of restoring service. This guide provides an operating model you can implement immediately. Prerequisites Incident severity model (SEV1, SEV2, SEV3). On-call owner for agent platform. Baseline observability for prompts, tool calls, and outcomes. Rollback path for model and policy configuration. Failure taxonomy 1) Intent misclassification The agent chooses the wrong plan for a valid request. Signals: - Wrong w...

Practical guide to layering guardrails (accuracy-first then risk-based) and using evals to cut hallucinations and ensure reliability in live agent deployments. Practical guide to layering guardrails (accuracy-first then risk-based) and using evals to cut hallucinations and ensure reliability in live agent deployments. 2026 production momentum shows 57%+ have agents live; addresses common failure modes with pragmatic, measurable controls. Includes controls, pitfalls, and a phased implementation path. Practical guide to layering guardrails (accuracy-first then risk-based) and using evals to cut hallucinations and ensure reliability in live agent deployments. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for mod...