PRDs Are Dead Unless You Improve Them (The Agentic AI PRD)

Here’s how you can stay engaged and access more valuable content: 🔹 Sign up for the next session series: https://maven.com/lls/87e612 🔹 LinkedIn Community: https://www.linkedin.com/company/mahesh-ai-pm-community/ 🔹 Substack : https://myaicommunity.substack.com/ 🔹 Sign up for Free Learning Resources: https://forms.gle/sRNjXnsurNxNAUQW7 🔹 AI PM Certification: https://maven.com/mahesh-yadav/genaipm 🔹 AI PM Interview Bootcamp: https://maven.com/mahesh-yadav/ai-pm-interview-prep-bootcamp 🔹 Speak to Our Mentors: Book a call Here: https://calendly.com/d/ct2s-3s2-n89/1-1-with-team-mahesh?month=2026-10&utm_medium=youtube&utm_campaign=description_campaign LLMs are trained to generate. Automation needs decisions. And when an LLM gets a decision wrong, its confidence is still 99%. PRDs are not dead. But a PRD written for a normal software product will not get an AI agent to production. In this session, Mahesh Yadav walks through why PRDs still matter, where today's PRD templates fall short for agents, and what to add across discovery, design, develop and deploy. He uses one running example, a legal agent that finds risks in a contract and extracts terms for downstream systems. What we cover: → Why teams need a PRD: alignment, scope, metrics, trade-offs and a record of intent → The standard PRD in four phases: discovery, design, develop, deploy → Discovery: why a public AI agent can't do the same job, your moat, and governance constraints → Design: trust-first layers (answer, plan, citations, feedback), hallucination guardrails, confidence thresholds and human in the loop → Develop: evaluation criteria for helpful, honest and harmless, ground truth data, tools and permissions, and a minimum evaluable product you can launch in 6 to 8 weeks → Deploy: monitoring, alert thresholds, fallbacks, rollout plans, and cost and ROI per user and per agent → A 31-point checklist you can upload to Claude to test any PRD → A Claude skill that drafts a PRD with the checklist built in Four problems a PRD for agents has to answer: how you stay ahead of the big labs, how you build trust, how you know quality before release, and how you control cost per user after launch. Session page: https://maven.com/p/84213a/agentic-ai-zero-to-hero-build-like-devs-copy-my-prd-md This session is part of the Agentic AI Zero to Hero series. Next week: observability for AI agents, covering evaluations, reliability and ROI. #AIPM #PRD #AgenticAI #ProductManagement #AIAgents