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...

How agentic AI enables hyper-personalised, real-time underwriting in specialty insurance, with trade-offs in data quality and regulatory compliance. How agentic AI enables hyper-personalised, real-time underwriting in specialty insurance, with trade-offs in data quality and regulatory compliance. Top 2026 insurance trend (hyper-personalisation, AI agents joining teams); relatable for your Verisk/specialty background. Includes controls, pitfalls, and a phased implementation path. How agentic AI enables hyper-personalised, real-time underwriting in specialty insurance, with trade-offs in data quality and regulatory compliance. 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 ...

Implementing runtime controls, fairness checks, and accountability in agent decisions for insurance and finance compliance. Implementing runtime controls, fairness checks, and accountability in agent decisions for insurance and finance compliance. 2026 focus on agentic guardrails in law and runtime ethics. Includes controls, pitfalls, and a phased implementation path. Implementing runtime controls, fairness checks, and accountability in agent decisions for insurance and finance compliance. 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 r...