Cohort Program

Agentic AI Product Management Certification +Anthropic Claude Certification Prep

Learn from #1 AI Instructor | Lead AI Strategy | Build, Get Certified, Get Hired

Categories: Agentic AI

Overview

10th cohort (longest running AI cert.) Rated #1 and has most 5 star reviews on Maven Mahesh has worked in all AI layers@ Google, Meta and AWS AI Mahesh teaches every class on whiteboard and uses real examples from his own experience of shipping agents in production.

What you'll learn

  • ML concepts of Neural networks basics; DL vs. traditional ML vs Agent context, Supervised, unsupervised, reinforcement learning.
  • Add needed context to your agents. See where vanilla RAG fails and Agentic RAG is needed, find limitation and learn evaluation of RAG.

Schedule

  • 2 weeks, 2 live sessions, one each week. The first is 08 Aug 2026, 12:00 PM GMT+8, and they run weekly after that.
  • Every session is recorded. You keep the recording for 30 days after each one, so you can catch up if you ever miss one live.

Curriculum

2 weeks · 2 live sessions · tap a week to see what you build

  1. Week1Session 108 Aug 2026

    Build PRDs, case-studies, and decision docs with AI first tenets that you can show managers, recruiters or investors.

    Build evaluation for AI models, Agents. Create LLM as judges, use AI evaluators and define matrices for qualitative analysis.

    PracticeFinalizing artifacts, hardening demos, and crafting a compelling narrative. Getting your agent in hand of customers and get feedback.
    Reviewedwritten feedback
  2. Week2Session 215 Aug 2026

    Understanding of the Responsible AI Framework, Risks that AI products face and how to mitigate them by building guardrails on your agents.

    Learn about tools usage, MCP and add integration using MCP to your agents.

    PracticeA mockup model
    Reviewedwritten feedback

Projects

A working Agentic AI Product

Assignments

  • Finalizing artifacts, hardening demos, and crafting a compelling narrative. Getting your agent in hand of customers and get feedback.

    Doing rehearsal runs, polishing evaluation dashboards and guardrail evidence

Feedback

I will write a written feedback

Capstone

A deployed agent AI

About Nathalia

Nathalia Nelson

Nathalia Nelson

Vetted by Maram

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Reviews

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FAQ

Who is this cohort designed for?

This program is built for product managers who want to work confidently on AI agent products, whether you are new to the space or already shipping features and want a more rigorous foundation. If you are curious about the Anthropic Claude certification, that preparation is woven in as well.

Do I need a technical background to keep up?

A technical background is not required, but comfort with product thinking and a genuine curiosity about how AI systems work will help you get the most out of it. Mahesh teaches on a whiteboard with real examples, so concepts are explained from first principles rather than assumed knowledge.

What does the weekly experience actually look like?

Each session is taught live by Mahesh, using a whiteboard format and examples drawn directly from his experience shipping agents at companies like Google, Meta, and AWS. This is not pre-recorded content or a guest speaker rotation.

What will I be able to do by the end of the program?

You will leave with a working understanding of how to scope, build, and evaluate AI agent products, along with preparation for the Anthropic Claude certification. The goal is practical capability you can apply immediately, not just familiarity with concepts.

How established is this program?

This is the tenth cohort of the program, making it one of the longest-running AI product certifications available. It has earned the highest ratings and the most five-star reviews on Maven.

Is this cohort right for me if I have already used AI tools in my work?

Yes, prior exposure to AI tools can actually help you connect the material to things you have already seen in practice. The program goes deeper than surface-level tool usage, focusing on how agents are designed and shipped in real product environments.

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