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...

A practical list of AI use cases for insurance operations, underwriting support, claims, and service workflows. Insurance teams do not need moonshot AI programmes to create value. They need targeted workflows with clear controls. 10 practical use cases Submission triage and document completeness checks. Broker email summarisation with action extraction. Underwriting note drafting from structured risk inputs. Claims intake classification and routing support. Policy wording comparison assistance. Renewal packet preparation and variance summaries. Internal knowledge retrieval for servicing teams. Meeting preparation briefs for account and placement teams. Escalation risk early-warning summaries. Portfolio-level trend summaries for leadership reviews. Delivery pattern that works Start with one workflow and one business metric. Add human-in-the-loop review for all externally visible outputs. Capture failure cases weekly and retrain prompt/process contracts. Exp...