As AI agents become more autonomous, engineering teams need more than static governance checklists and disconnected evaluations to ship safely. In this webinar, Karan Nisar, Staff AI Solutions Engineer, Weights & Biases by CoreWeave, will demonstrate how to operationalize AI governance with a repeatable review workflow that combines automated evaluations, red-team testing, and human review into a single evidence-backed system for AI release decisions.
What to expect
A practical look at why traditional Responsible AI workflows struggle to keep up with modern AI agents, and what scalable, production-ready review processes look like instead
Strategies for structuring evaluations, red-team testing, and release reviews across the AI development lifecycle
Real-world examples of automated evaluations, LLM-as-judge scoring, and adversarial testing techniques used to uncover issues like jailbreaks, PII leakage, hallucinations, and bias
A live demo showing how W&B Weave captures traces, evaluations, and human review decisions to create an auditable system of record for every release
What you'll learn
How to build production-ready, AI governance workflows that combine automated evaluations, red-team testing, and human review into a scalable release process for AI agents
Best practices for using W&B Weave to investigate failures, compare releases, and track changes to prompts, guardrails, and model behavior over time
Practical techniques for creating evidence-backed AI review processes that help engineering teams catch issues earlier, improve release confidence, and maintain audit-ready decision records
Who should attend
AI leaders and their teams, including AI researchers, AI engineers, MLOps engineers, platform engineers, responsible AI and governance practitioners, AI product managers, and security teams focused on AI risk.
Webinar
