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Masterclass · Self-paced

Ship agentic AI that works.

Move past the ReAct tutorials. Build production agents with proper loops, multi-agent coordination, evaluation harnesses, and the observability that catches regressions before your users do.

Enroll in masterclass

Still deciding? Ask first.

Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.

  • Curriculum fit, prerequisites, or where to start
  • Honest answer, no pressure to enroll
“Finally understand how agents actually work, not just the framework magic. Shipped an agentic system my team relies on.”
Backend Engineer · SaaS

Engineers are learning here from

NVIDIAMICROSOFTGRABWISEPIPEDRIVEBOLTGLIA

Most agent content teaches one framework, once, on a toy demo. That's not what ships. This masterclass teaches the loop from scratch, then the production patterns on top: LangGraph for graphs, supervisor patterns for multi-agent, eval harnesses for safety, tracing for debugging.

By the end you will have a deployed agent you can demo, with traces you can debug and evals you trust. Lifetime access.

Curriculum

Ship an agent that runs reliably in production.

  1. 01

    Agent fundamentals from scratch

    ReAct loop, tool selection, context management, failure recovery. Build the primitives without a framework so you understand what frameworks hide.

  2. 02

    LangGraph for production

    Checkpointing, branching, observability hooks, conditional edges. Graduate from scripts to production-ready agent graphs.

  3. 03

    Multi-agent coordination

    Supervisor patterns, hand-off between specialists, context compression. Ship a research + writer system that actually converges.

  4. 04

    Evaluation and observability

    Fixture tests, trace recording, regression alerts. The sleep-at-night layer your agents cannot go to prod without.

Outcomes

You finish able to:

  • Design an agent loop that recovers from tool failures
  • Pick between ReAct, plan-and-execute, and multi-agent for a given task
  • Wire LangGraph into a production Python service
  • Build an eval harness that catches regressions across model versions
  • Ship a deployed agent you can show in an interview

Who it's for

Is this for you?

Software engineers

who understand AI basics and want to build real agentic systems

AI engineers

who want hands-on experience with multi-agent architectures

Backend developers

looking to add agentic AI capabilities to existing products

Pricing

Invest in one complete agentic system.

One masterclass. One complete agentic system. Lifetime access.

FAQ

Frequently Asked Questions

What prerequisites do I need?
Basic Python knowledge and familiarity with LLM concepts. Ideally, you have taken the Building AI Agents resource or have equivalent experience.
What will I build?
A complete agentic RAG system that converts natural language to SQL, auto-generates visualizations, and runs on a LangGraph multi-agent architecture.

Ship an agent. Not another bookmark.

Self-paced. Lifetime access. One deployed system by the end.

Enroll in masterclass

Agentic AI Masterclass

Self-paced masterclass

Enroll