Large Changes with Coding Agents: Plan, Split, Verify
Use coding agents for migrations and cross cutting changes without losing control of a big codebase
A taste of a lesson
We need to replace a deprecated logging library in about 600 files. Should I give the whole job to an agent?
Not as one job. First, inventory the call patterns; most will be a few uniform forms, like simple info and error calls, and those suit a codemod, which the agent can help write and you can run deterministically. The remaining unusual cases, such as custom formatters or context managers, go to the agent in batches by module, with your before and after examples in a plan file. Each batch becomes its own pull request with tests passing. How many distinct call patterns does a quick search show?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Scope a large change by inventorying occurrences and grouping patterns
- Write a migration plan with examples, order, compatibility and rollback
- Choose codemods, scripts or agents for each pattern
- Split work into reviewable batches that each pass tests independently
- Coordinate parallel agent sessions and roll out in stages
Lesson plan
- 1 Is the codebase ready? Assess test coverage and type safety before automating a large change. Start
- 2 Inventory and the migration plan Find every occurrence and write a plan with examples and rollback. Start
- 3 Codemods, scripts and agents Pick the right tool for each kind of change. Start
- 4 Batching for review Split the work into pull requests people can genuinely review. Start
- 5 Parallel sessions and consistency Speed up with several agent sessions without inconsistent results. Start
- 6 Verification and staged rollout Prove the change works and release it safely. Start
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About this tutor
For experienced developers and tech leads considering coding agents for large work: framework upgrades, API migrations, renaming across many modules, adding logging everywhere, or converting a codebase to a new pattern. Agents can grind through repetitive changes at scale, and they can also produce enormous diffs nobody can review. You will learn to scope large changes, write a migration plan, split work into reviewable batches, use codemods and scripts alongside agents, run parallel tasks carefully, verify with tests and type checks, and roll out changes in stages.
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About the teacher
I teach developers and beginners the daily habits that make AI coding assistants useful instead of risky
9 tutors 411 lessons taught Sample
I am a software developer who teaches the working habits around AI coding assistants: how to describe a task, how to pair with an assistant without losing the thread, how to use a terminal based coding agent, and how to write project instruction files that keep it on track. I teach from the keyboard. We open a small project, try...
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