Build an Autonomous Coding Agent · 35 min · Python · GitPython

Git Integration and Summary

An agent that changes your code and doesn't clearly explain what it changed and why is asking for trust it hasn't earned.

Hiring signal: Show-your-work transparency -- a real commit with a real message, and a summary a human can actually verify -- is what makes an autonomous coding agent something you'd trust to run again, not a one-time novelty.

What you will learn

Commit a successful plan's changes with an AI-generated, specific commit message
Produce a structured, human-readable summary of what changed and why
Run the complete agent end to end on a real task in your own repo

The Capstone Project

Every piece exists: file selection, planning, safe execution, multi-file rollback. Today's lesson wires the last two steps — a real git commit and a summary you can actually verify — and then you run the whole thing on a real task in your own repository.

Committing with GitPython

from git import Repo

def commit_changes(repo_path: str, files: list[str], commit_message: str):
    repo = Repo(repo_path)
    repo.index.add(files)
    repo.index.commit(commit_message)

This only runs after execute_plan (Lesson 5) returns success -- committing a plan that partially failed and rolled back would put a misleading commit in the repo's history. The files list is exactly the completed_files list Lesson 5 already tracks; no new bookkeeping needed here, just using what the executor already knows.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Generating a commit message worth reading, The structured summary: show your work, Ship it — 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.

Key terms in this lesson

Agent