Build an Autonomous Coding Agent · 30 min · Python · Anthropic Python SDK
Coding Agent Architecture
A coding agent isn't a chat agent with a file-write tool bolted on -- the codebase itself is context that has to be curated, not dumped.
Hiring signal: Understanding how Claude Code, Cursor, and Devin actually work under the hood -- plan, select context, execute, test, iterate -- is what separates someone who uses these tools from someone who can build and customize their own.
What you will learn
- Explain why coding agents need a codebase-understanding layer that general agents don't
- Describe the plan -> execute -> test -> iterate loop this course builds
- Understand the token-cost reality of running a coding agent before writing any code
Introduction
Course 01 built an agent that calls tools in a loop. Today you start building something structurally different: an agent whose "environment" is an entire codebase, whose actions have to be safe to apply and reversible, and whose success criteria is "the tests still pass," not "the model said it's done."
What You're Building
Nothing that touches a real codebase yet. Today is the project skeleton, one file-read tool, and — just as importantly — an honest look at what this course costs to run, before you're 3 lessons deep and surprised by an API bill.
Budget for this before you start, not after
A single realistic task through the finished agent (Lesson 6) typically costs 50k-200k tokens — at Sonnet pricing, roughly $0.50-$2.00 per run, and you will run it more than once while debugging. This isn't a course to work through with your API key on a whim; know the cost before Lesson 4's fix-loop starts burning tokens on retries.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Why "dump the whole repo into context" doesn't work, The loop this course builds, end to end, What you're building today — plus a hands-on lab, quiz, and project artifact.
Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.
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