Agentic AI Engineering for Large Lagacy Projects

About this course

Every AI coding tool promises the same thing: write a prompt, get working code. Then you point it at a real Enterprise project, ten thousand files, five years of history, conventions nobody ever wrote down, and it just guesses. Confidently. Wrong.

This course is a practical, three-section system for making AI coding agents actually work on large, brownfield, and legacy codebases, built from real training I've delivered to engineering teams, including Fortune 500 clients.

Section 1: Agentic Engineering & Context Management

What agentic engineering actually is, and how it differs from just prompting a chat window. You'll see how AI spans the full software development lifecycle at each of its three levels (Assisted, Augmented, Agentic), why naive AI use falls apart on large and legacy projects specifically, and how to treat your context window as a real, limited budget instead of a bottomless bucket. This section ends with Context Engineering as a discipline: the Memory Bank pattern and a full documentation hierarchy that gives any AI agent instant, reliable project context.

Section 2: Rules, Agent Skills & MCP

The tools that actually shape how an AI agent behaves. You'll learn to design a rule hierarchy that scales from a whole codebase down to a single file pattern, describe a legacy architecture so AI stops proposing incompatible fixes, write role-based rules and negative constraints that cut hallucination dramatically, and know exactly when a plain rule is enough, when you need an Agent Skill, and when only an MCP server will do, including how to evaluate one safely before connecting it to production.

Section 3: Spec-Driven Development

How to move from ad-hoc prompting to a disciplined, spec-first workflow for refactoring and shipping changes safely on a codebase you didn't write.

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