Technical Reference | AI Engineering Playbooks

A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. Your stakeholder alignment and regulated-environment experience makes this a natural and credible topic. Includes controls, pitfalls, and a phased implementation path. A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. 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. Basel...

Tools and processes to discover/secure unmanaged agents in regulated orgs. Tools and processes to discover/secure unmanaged agents in regulated orgs. 2026 governance priority. Includes controls, pitfalls, and a phased implementation path. Tools and processes to discover/secure unmanaged agents in regulated orgs. 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 model and tool behaviour. Defined quality and security acceptance criteria. Practical approach Define the business decision this capability supports. Limit the first release scope to one workflow and one owner. Add measurable controls for quality, latency, and failure handling. Roll out with explicit monitoring and rollback paths. Implementat...

Map key standards to production guardrails for agents in regulated sectors. Map key standards to production guardrails for agents in regulated sectors. Top governance best practices trending. Includes controls, pitfalls, and a phased implementation path. Map key standards to production guardrails for agents in regulated sectors. 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 model and tool behaviour. Defined quality and security acceptance criteria. Practical approach Define the business decision this capability supports. Limit the first release scope to one workflow and one owner. Add measurable controls for quality, latency, and failure handling. Roll out with explicit monitoring and rollback pat...