This talk covers spec-driven development as a way to give agents better constraints, large language model (LLM) gateway and routing strategies for managing which model handles which task, test-driven development adapted for an agent-first workflow, and verification practices built for the volume and pace of AI-generated code.
In this talk, I will share how our platform engineering team made a deliberate investment to simplify service-to-service connectivity by introducing uniform platform abstractions and out-of-the-box discovery.
This talk explores that journey: how we built testing pipelines for non-deterministic systems, defined “ethical success criteria,” and aligned engineers, product managers, and legal teams around shared principles for responsible AI.
This talk explores why traditional code review is struggling to keep up with AI-driven development. It introduces a five-layer trust model designed to help engineering teams validate AI-generated code, reduce reliance on manual review, and ship faster without sacrificing quality or control.
This talk explores how to turn a divided technical evaluation into a decision everyone can trust. Through a real-world AI code review tool rollout, you’ll learn a practical framework for setting shared criteria, rebuilding developer confidence, and making technical decisions with genuine stakeholder buy-in.
This session walks through an engineering framework for accountable autonomy: a three-tier trust model that categorizes decisions by blast radius (impact and reversibility), an autonomy budget that meters agent trust with SLOs and revokes it automatically when behavior degrades, and circuit breakers that freeze an agent before a bad pattern becomes an incident.