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Agentic AI Engineering Guide

A FREE 6-day email course to reveal the 6 critical mistakes that silently destroy agentic systems.​
​(Even if you already know how AI agents work)

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    WOULD BE AMAZING TO PUT A TESTIMONIAL FROM REPUTABLE PERSON (E.G., Chip Huyen, "Towards AI is a sound place for learning AI" - WE CAN ALSO USE THEIR IMAGE

    Feng Mian

    Chicago, IL

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    Here's what you get inside:

    Mistake #1: Treating the context window as an afterthought.

    Mistake #2: Starting with complicated solutions.

    Mistake #3: Building agents when a workflow will do.

    Mistake #4: Fragile parsing of LLM outputs.

    Mistake #5: Forgetting agents need planning.

    Mistake #6: Not starting with AI evals from day zero.

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    Tired of building AI agents that work, but become harder to debug, trust, and scale over time?

    We've spent 2+ years building and operating agentic AI systems in production. Along the way, we've seen a similar pattern repeat: most agent failures stem from a handful of subtle system design mistakes. So we’ve put together this free 6-day email course to help you:

    • Identify the architectural mistakes that quietly break agents in production
    • Reason about why agentic systems become unpredictable, expensive, or brittle
    • Decide when agents are justified versus when workflows are the better choice
    • Understand what planning loops require (and why naive tool loops fail)
    • Evaluate agent behavior using concrete criteria instead of intuition

    By the end of the email course, you’ll have a clear mental model for building production-ready agentic AI systems, without over-engineering or guesswork.