Maxxwell by Rindler
Writing

A safe, repeatable AI refactoring workflow

2026-09-25

AI coding IDEs are good enough now that the problem isn’t “can it write code?”-it’s “can I trust what it changed across 40 files while I was in a meeting?”

AI coding IDEs are good enough now that the problem isn’t “can it write code?”-it’s “can I trust what it changed across 40 files while I was in a meeting?”

Stack Overflow’s 2025 survey says 84% of developers use or plan to use AI tools, but 46% distrust AI accuracy vs. 33% who trust it. DORA’s 2024 report even sees a 7.2% drop in delivery stability with higher AI adoption when teams don’t adjust their workflow.

This tutorial walks through a concrete way to push against that: a repeatable AI-assisted refactoring loop in Maxxwell, with scoping, diff gates, and rollback wired in.

If you care about the bigger picture of “agent-native development”, there’s a related deep-dive in the pillar piece, Agent-native development: a working definition. Here we’re staying practical.

What “reliable” means for AI refactoring

For refactors, “reliability” isn’t about the model’s IQ. It’s about:

Surveys back this up:

Refactoring is attractive because it’s structured. It’s dangerous because it’s global. The rest of this tutorial is about using Maxxwell to keep that under control.

Prerequisites and baseline setup

You’ll need:

1. Start Maxxwell and connect your agents

Open Maxxwell and add your providers:

Bring your own key: configure the same model access your agents already use.

Maxxwell does not replace your tools. It runs them as real terminal sessions:

2. Create a refactoring “fleet” for this repo

Start Maxxwell in the repo you want to refactor and add worker sessions for it. In the UI you’ll see:

The state labels are the first safety check: they stop you assuming “working” when the process is actually waiting for input or stuck on auth.

Step 1: Scope the refactor in the orchestrator

Maxxwell gives you an orchestrator seat on top of the fleet. It’s itself a real agent session, started from a brief. This is where you pour the intent.

3. Write a tight refactoring brief

Open the orchestrator in Maxxwell and give it a written goal. For example:

You are coordinating a refactor of the `billing-core` module in this repo.

Goal: Introduce a Currency type instead of passing raw strings like "USD" around.

Hard scope:
- Only touch code under `billing/` and `shared/currency/`.
- No changes outside those directories.

Safety rules:
- For each change batch, create or update tests first.
- Keep changes per commit small and logically grouped.
- Never commit or push; I will review diffs and run git commands.

Deliverables:
- A plan listing discrete steps.
- Worker prompts to implement each step.
- A summary report of what changed and which tests you expect me to run.

This addresses the biggest complaints from the surveys-“almost right” and “hard to debug”-by constraining where the agent may act.

4. Ask the orchestrator for a stepwise plan

Send a prompt:

Scan the repo, then propose a 5-7 step refactoring plan that stays inside the hard scope.

Each step should:
- Name the files or patterns you expect to change.
- Describe the tests you expect to adjust or add.
- Fit into a single small commit.

Return the plan only; do not start coding yet.

You should get back something like:

  1. Introduce Currency type in shared/currency/.
  2. Replace raw strings with Currency in billing/invoices/*.go.
  3. Update validation logic in billing/validation/*.go.
  4. Adjust tests under billing/tests/.
  5. Run test suite and clean up dead helpers.

The point isn’t perfection. It’s to establish waypoints you can align the workers around.

Step 2: Use workers for bounded change batches

Now turn the plan into parallel work, without letting it slip into an uncontrolled “change everything” mode.

5. Assign steps to worker sessions

Tell the orchestrator to draft worker instructions:

For each plan step, draft a short instruction for a coding agent that will
run in this repo. Include:
- Exact files/globs it may touch.
- Tests to run.
- Reminder not to commit.

Return them as numbered tasks.

Copy each instruction into a different worker. Or, from within Maxxwell, use a fleet control to broadcast:

Every worker runs your agent (Claude, Cursor, etc.) in a real terminal, so you still see actual shell commands and editor sessions, not an abstract UI.

If a worker drifts-starts touching files outside scope-you can:

Maxxwell itself does not auto-correct drift. It gives you visibility and a clean way to intervene.

Step 3: Make diff review a hard gate

The biggest failure mode in the DORA data is larger, less-reviewed change batches. So treat “review diffs” as the gate between each step and git commit.

6. Establish a diff review protocol

Instruct the orchestrator:

After each worker finishes a step, I want to:
- See the full `git diff` for only the files in scope.
- Get a short, file-by-file rationale for the changes.
- Decide whether to keep, edit, or discard.

Draft the git commands I should run for this review after each step.

You might get something like:

# Show diff for scoped directories only
git diff -- billing/ shared/currency/

# For a specific step, e.g., invoices only
git diff -- billing/invoices/

Run the diff commands yourself. Then ask the orchestrator:

Summarize this diff by file. For each file, answer:
- What changed in plain language.
- How this supports the refactor goal.
- Any obvious risks or missing tests.

Here is the diff:

...paste diff...

You now get:

7. Use Maxxwell’s “drafts rather than acts” for fleet-wide commands

Maxxwell’s fleet controls follow a simple rule: they draft, they do not execute.

For example, a fleet control that would change the fleet writes an unsent sentence into the composer and stops, rather than acting. The same principle applies to anything you would otherwise run blind:

git add billing/invoices/ shared/currency/

But it will not run it. You stay the one who presses Enter.

That sounds small, but it’s the difference between:

This matches what developers keep asking for in the surveys: control and verification, not blind speed.

Step 4: Build rollback into the workflow from the start

Rollback is the other half of reliability. You want to be able to say “this batch is garbage, get me back to just before Step 3” and have it be a trivial action.

8. Create a safety branch and tags

Before you start refactoring, from a clean state:

git checkout -b refactor/currency-type

git tag before-currency-refactor

Tell the orchestrator this exists and that all advice should assume you’re on that branch.

After each accepted step, commit with a systematic message:

git commit -am "refactor: introduce Currency type in billing-core (step 1)"

Then create lightweight tags for milestones:

git tag refactor-step-1

Now rollback is a one-liner:

git reset --hard refactor-step-1
# or
git reset --hard before-currency-refactor

Have the orchestrator draft these commands for you, but again, it’s your finger on Enter.

9. Ask the orchestrator to maintain a “rollback ledger”

One persistent pain in AI workflows is not knowing what you’re rolling back. Fix that by asking:

Maintain a running log of each accepted step with:
- Git commit hash (I will paste it in).
- Tag name.
- One-line description of the behavioral change.
- Test commands I ran successfully.

Use this as a "rollback ledger" so I can quickly see what I'm reverting.

Each time you commit, send the hash and let the orchestrator update the ledger:

Step 2 committed as 7f3c9a1. Tests run:
- `go test ./billing/...`

Update the ledger.

This gives you a human-readable timeline to compare with git log, and it keeps AI changes out of the “mystery meat” category.

Step 5: Wire safety checks into the loop

Developers in the ICSE and Stack Overflow surveys are happiest when AI writes tests and boilerplate, least happy when it improvises architecture. Refactoring sits in between, so push tests and checks to the front.

10. Make tests first-class work items

From the orchestrator, insist on tests as explicit steps:

For each refactoring step, I want a dedicated test task:
- If tests exist, list the exact test files and cases to update.
- If tests are missing, propose specific new files.
- Never touch production code in a "test" step.

Rewrite the plan to interleave code and test steps.

Assign those test steps to a worker dedicated to tests. This matches GitHub’s enterprise data: 92% of devs use AI to generate tests, and it’s one of the most reliable use cases.

11. Add context-pressure awareness

Large refactors can hit context limits in the underlying models. Maxxwell exposes a live context-pressure readout with tiered warnings per session.

When you see warnings:

This worker is hitting context limits.
Propose a minimal summary of the prior steps that we can paste into a new
session so we don't lose important context, but stop carrying irrelevant detail.

Then restart the worker with just that summary, plus the current step.

Maxxwell will not auto-compact or recycle context for you, but it makes it visible when you need to intervene.

Step 6: Summarize the refactor before it lands

Before you merge anything upstream, get a clear summary of what actually changed.

12. Use the orchestrator for a return report

Ask:

Using our rollback ledger and the final diffs, produce a concise refactor report:
- High-level goal.
- List of steps with commit hashes.
- Breaking changes or migration notes.
- Commands for rerunning the key test suites.

Assume this will be pasted into a pull request description.

This gives you a PR body that:

It lines up with how tools in this space are positioning reliability: approvals, logs, and clear rollback paths.

How this compares to doing it all in an AI IDE

You can approximate some of this in a single AI-powered IDE session, but Maxxwell changes a few important mechanics:

If you’re already managing two or three agent sessions by hand with tmux or bespoke scripts, Maxxwell is basically “orchestration with a brain”, without giving up any of your existing tools.

FAQ: Reliability of AI coding IDEs and Maxxwell refactoring workflows

How does this workflow address AI reliability issues developers report?

It constrains where agents can act, enforces diff review gates, and makes rollback cheap. That’s the antidote to “almost right” code and mysterious large changes: scoped steps, readable diffs, and commit-level checkpoints you can reset to.

Can Maxxwell automatically stop an agent that drifts or breaks tests?

No. Maxxwell does not auto-detect drift, auto-restart sessions, or run periodic goal checks. It surfaces each session’s state, context pressure, and recent activity so you can spot drift quickly, then you decide whether to stop or redirect the worker.

How is this different from just using GitHub Copilot or Cursor in my IDE?

Those tools are excellent single-IDE experiences, with their own guardrails. Maxxwell sits above them: it manages multiple agents across terminals, adds an orchestrator that works from a written brief, gives you per-session state, and ensures fleet controls draft commands rather than executing them.

What are best practices for scoping AI-assisted refactors?

How do I roll back AI-generated changes safely?

Create a dedicated refactor branch and tag key milestones. After each accepted step, commit with a clear message and tag it. Maintain a simple ledger in the orchestrator with commit hashes, tags, and tests run. If something goes wrong, use git reset --hard <tag> or git revert to back out the bad batch.

If you want to see how this fits into a wider “agent-native” stack-builds, CI, review loops-the Agent-native development: a working definition piece goes through the architectural picture. The workflow above is a small, concrete slice of that: one repeatable, safe way to let agents refactor your code without quietly wrecking your repo.