The PM’s Guide to AI Agent Observability - Traces, Evals, Governance

Sign up for the FREE Session: https://maven.com/p/9557c9/secure-ai-agents-against-prompt-injection-as-a-pm?utm_medium=ll_share_link&utm_source=instructor Most teams ship AI agents after a demo works. 85% never make it to production. The reason is not the model. It is not the code. It is that nobody set up observability before launch. In this session, Mahesh Yadav — former AI PM at Google, Meta, AWS, and Microsoft — walks through what observability actually means for product managers, and what needs to go into your PRD before a single line of agent code is written. What this session covers: — Why agents fail silently and confidently, while traditional software fails loudly — The three pillars every PM must spec before launch: Traces, Evaluations, and Governance — How to build a debug and playback mode so you can replay any agent session and see every tool call — Why the same agent passed 66% on run one, 46% on run two, and 6% on run three — and what that means for your eval strategy — How to build a golden dataset with SME-vetted ground truth and set your pass/fail bar before the agent does — What governance requirements catch prompt injection, tool misuse, and out-of-scope agent behavior before your users find it first — How to set up red teaming on your own agent before launch Labs from this session: Foundry eval lab: Score your n8n agent on five metrics with a judge model Red teaming lab: Attack a live n8n chatbot and log every attempt in Foundry