AI Code Rewrite Trial Runs: A Founder Checklist Before Agents Touch Production

The most dangerous AI coding demo is the one that works once.

A founder asks an agent to refactor a payment flow, migrate a database schema, replace an authentication library, rewrite a crawler, or "clean up" a product built over six intense weeks. The agent produces a confident plan, edits a lot of files, fixes the obvious errors, and the app boots locally. For a moment, it feels like leverage.

Then the real product starts speaking.

The password reset link no longer handles old users. A webhook signature check silently changed. A timezone edge case breaks invoices for customers outside the founder's own region. A retry loop now calls a paid API three times. A background job still compiles, but it no longer writes the audit record that support needs when a customer complains.

The problem is not that AI coding agents are useless. The problem is that large changes create hidden product risk, and AI makes large changes cheap enough that founders can create that risk before they have the review system to absorb it.

This guide gives non-technical founders a practical operating rule:

Before an AI agent rewrites production code, make it pass a small trial run, an independent adversarial review, and a product-specific release gate.

That is still lighter than recovering from a broken launch. The goal is to separate safe assistance from unmanaged autonomy.

Why This Matters Now

On July 8, 2026, Bun's Jarred Sumner published a detailed account of rewriting Bun from Zig to Rust with Claude Code. The post is worth reading because it is not a generic AI productivity claim. It describes a serious engineering migration with constraints, tests, false starts, review loops, compiler-error work queues, CI gates, security review, fuzzing, and production validation.

The headline numbers are striking: roughly 50 dynamic workflows, 11 days of continuous agent work, thousands of commits, all major platforms green before merge, no skipped or deleted tests, and a large bill that would be unrealistic for most small teams. But the more useful lesson for founders is not "AI can rewrite anything."

The useful lesson is the opposite:

The rewrite became plausible only because the team wrapped the agents in process.

The Bun post describes preparation documents, a tiny trial run on three files before translating the whole codebase, separate implementer and reviewer contexts, adversarial review that actively looked for bugs, the existing test suite as a language-independent oracle, CI across platforms, and follow-on security and fuzzing work. Simon Willison's summary called out the sophistication of the workflow, including dynamic workflows, trial runs, and adversarial review. That is the part a founder should copy.

Most YBuild readers are not rewriting a JavaScript runtime. You may be launching a booking app, internal dashboard, AI support tool, lead enrichment workflow, marketplace, or document automation product. Your codebase may have been assembled with an AI app builder, a freelancer, templates, and your own prompts. You probably do not have a million assertions.

That makes the process more important, not less.

When the test suite is thin, the founder must create smaller gates. When the codebase is young, the founder must protect the few behaviors that already matter. When the team is non-technical, the founder must turn "review the code" into concrete product checks that can be observed.

Start With a Change Class

Do not begin by asking whether the AI agent is good. Begin by classifying the change.

Use four classes:

Change class	Examples	Default rule
Cosmetic	Copy, spacing, icons, simple style changes	Agent can edit; human previews
Local functional	One form, one page, one API route, one email template	Agent can edit after a small test plan
Cross-cutting	Auth, billing, database models, permissions, shared components, analytics	Trial run and adversarial review required
Irreversible or high-trust	Deleting data, changing legal records, payments, medical, financial, identity, security, customer communications at scale	Do not ship without expert review and rollback plan

The class matters because AI agents make every task feel like a prompt away. That feeling is false. Changing a button label and changing how invoices are generated are not the same product decision.

If you are not sure which class a change belongs to, assume it is the higher-risk class. A founder does not need to understand every implementation detail to ask: "If this goes wrong, can we notice quickly, undo it, and explain it to a customer?"

Write the Behavior Contract Before Code

Before the agent edits anything, write a short behavior contract in plain language. This becomes the anchor for the trial run and review.

For example: existing users must still be able to sign in, canceled subscriptions must not be charged again, webhooks must reject invalid signatures, admin-only pages must stay protected, imported documents must preserve owner IDs, and preview mode must not send real external API calls.

This is not a full technical specification. It is a list of promises the product already makes.

AI agents are often good at local implementation and weak at unstated context. If the behavior contract is not written down, the agent may optimize the visible request while damaging an invisible promise. The contract gives implementer agents, reviewer agents, and humans the same target.

OpenAI's evaluation guidance makes a similar point for AI applications: define the task, run tests with relevant inputs, analyze results, and iterate. It also warns against "vibe-based evals." For code changes, the equivalent mistake is shipping because the app "seems fine" after a quick local run.

Run a Three-File Trial Before a Big Rewrite

The most transferable lesson from the Bun rewrite is the trial run.

Before translating 1,448 files, the Bun workflow started with three files. That small batch tested whether the porting guide, lifetime analysis, review loop, and fixes were coherent before the system scaled.

Founders should copy the pattern even for much smaller products.

If you want an agent to migrate a UI library, start with one low-risk page, one medium-complexity component, and one shared component that has tests or obvious user flows. If you want to change a database model, start with one non-critical table or one read-only pathway. If you want to replace an API provider, start with a sandbox route, not the live checkout or production email sender.

The trial run has three jobs:

Show whether the agent understands the codebase.
Reveal the types of mistakes it makes.
Improve the instructions before the expensive part begins.

A failed trial run is not wasted time. It is a cheap warning. The correct response is not to manually patch every issue and continue the large rewrite. The correct response is to fix the process: the instructions, tests, context files, permissions, and review checklist.

Use this rule: If the trial run creates failures you cannot explain, stop the rewrite. You can still use the agent for smaller tasks. You are not ready for the large change.

Separate the Implementer From the Reviewer

One of the strongest details in the Bun post is the use of separate agent contexts: one agent implements; other agents review adversarially. The reviewer is not asked to be nice. It is asked to find why the change is wrong.

This matters because the agent that wrote the code is biased toward completing the task. Humans have the same bias. An agent can explain its own work fluently and focus on proving completion.

For founder workflows, the pattern is simple:

Ask one agent to implement only the trial change.
Ask a second agent, in a fresh chat or separate tool session, to review only the diff against the behavior contract.
Ask the reviewer to assume the change is wrong until proven otherwise.
Ask for failure cases, security concerns, data-loss risks, missing tests, and user-facing regressions.
Do not let the reviewer apply fixes in the same pass.

Then give the reviewer findings back to the implementer.

GitHub Copilot code review and Claude Code Review both follow versions of this idea: AI review comments do not replace required human approval, and review behavior can be shaped with repository instructions. GitHub's docs note that Copilot review leaves comments rather than approving or blocking a pull request. Anthropic's Claude Code Review docs describe multi-agent analysis that looks for logic errors, security vulnerabilities, edge cases, and regressions, while keeping existing review workflows intact.

That limitation is useful. Treat AI review as a bug-finding input, not as permission to merge.

Build a Product-Specific Gate

Founders often ask for "tests" as if tests are one thing. They are not. A useful gate combines automated checks, manual checks, and business-specific checks.

For an AI-built web app, use five layers.

1. Static and Build Checks

These are the basics:

The project installs cleanly.
Linting and type checks pass if the project has them.
The production build succeeds.
There are no new secret keys, test tokens, or local file paths in the diff.
The agent did not change deployment config unless that was explicitly requested.

These checks catch obvious damage. They do not prove the product works.

2. Behavior Contract Checks

Turn each behavior promise into a check. If the founder cannot automate it yet, write it as a manual release checklist.

For example:

Create a new account.
Log in as an existing user.
Trigger the webhook with valid and invalid signatures.
Upgrade, cancel, and reactivate a subscription in sandbox mode.
Export a document and compare the file name and owner.
Confirm a normal user cannot open the admin route.

The checklist should be boring; it prevents the founder from testing only the happy path shown in the demo.

3. Data Safety Checks

Any change touching persistence needs a data plan.

Ask:

What data can this change create, update, or delete?
Can the change be rolled back without losing user work?
Is there a backup before migration?
Does the new code handle old records?
Does it handle empty records, duplicate records, and partially migrated records?
Are background jobs idempotent if retried?

If you cannot answer these questions, do not let an agent run a production migration. Use a staging copy, export, or dry run first.

4. Observability Checks

A release gate should include how you will know the change failed.

For a small product, that may be simple:

Error dashboard checked after deploy.
Payment provider events checked after test purchase.
Support inbox watched for the first day.
Key workflow logs sampled.
Agent/tool traces reviewed if the product uses AI internally.

NIST's Generative AI Profile frames AI risk management as part of the product lifecycle, not a one-time model choice. The same idea applies here: a coding-agent change is not safe because it merged. It is safer when the team can monitor real behavior and respond.

5. Rollback Checks

Before shipping, ask:

What exact command or platform action reverts the deploy?
Would reverting code also require reverting data?
Is there a feature flag or canary path?
Who is allowed to make the rollback decision?
What user message will we send if the failure is visible?

The rollback plan does not need to be dramatic. It needs to exist before the founder is under pressure.

Watch for AI-Specific Failure Modes

AI code changes fail in familiar software ways, but some patterns are more common when the author is an agent.

Plausible cleanup. The agent removes code that looks unused but encodes a business exception, a compliance rule, or a legacy customer path. Silent contract drift. Function names remain similar, but return values, error handling, sorting, permissions, or retry behavior change. Over-broad fixes. The agent fixes one bug by changing a shared helper used by many workflows. Test theater. The agent adds tests that match its new behavior rather than the product's required behavior. Mock success. The local demo works because the agent mocked an external provider, skipped a real webhook, or used sample data. Permission widening. A route, token, storage bucket, or database rule becomes easier for the code to use and easier for the wrong user to abuse. Cost leakage. A loop that was local becomes an external model call, vector search, email send, or paid API request. Review collapse. The same agent writes, reviews, fixes, summarizes, and recommends merge, leaving no independent challenge.

OWASP's Top 10 for LLM Applications is aimed at LLM apps rather than coding workflows, but several categories map directly to agent-built products: insecure output handling, supply chain risk, sensitive information disclosure, excessive agency, and overreliance. A coding agent that can edit dependencies, call tools, and change deployment behavior should be treated as a powerful actor, not a text generator.

A Founder-Grade Prompt Pattern

Use this pattern before cross-cutting changes:

text

We are considering a production code change.

Goal:
[Describe the change in one paragraph.]

Behavior contract:
- [Promise 1]
- [Promise 2]
- [Promise 3]

Change class:
[Cosmetic / local functional / cross-cutting / irreversible or high-trust]

Trial run:
Change only these files or this low-risk workflow first:
- [file or workflow]

Constraints:
- Do not change deployment configuration.
- Do not change database schema unless explicitly requested.
- Do not remove existing tests.
- Do not skip failing tests.
- Explain any behavior change before making it.

Output:
1. Plan
2. Files likely to change
3. Risks
4. Trial-run implementation
5. Tests and manual checks

Then use a separate reviewer prompt:

text

You are reviewing this diff adversarially.

Assume the change is wrong until proven otherwise.
Compare it against the behavior contract.
Look for:
- user-facing regressions
- data loss or migration risk
- security or permission changes
- missing tests
- hidden cost increases
- changed behavior outside the stated scope

Do not fix the code. Return findings with severity and reproduction steps.

The second prompt is where many founders find the real value. It turns the agent from a builder into a skeptical reviewer.

When Not to Attempt the Rewrite

Some projects are not ready for agent-driven rewrites.

Do not attempt a large AI rewrite if:

You cannot describe the current product promises.
There is no staging environment or preview deploy.
The change touches payments, identity, health, legal, financial, or destructive data flows and no expert can review it.
The app has real users but no error monitoring.
The agent proposes deleting tests, skipping tests, or replacing a failing test with a weaker one.
The trial run changes files outside the agreed scope.
You cannot tell whether the trial run preserved behavior.
The business cannot tolerate a rollback window.

This is not anti-AI. It is pro-product.

AI coding agents are strongest when they operate inside boundaries: clear tasks, good context, existing tests, human review, and reversible release paths. They are weakest when asked to replace judgment.

The Real Lesson From the Bun Rewrite

The Bun rewrite is impressive because it shows what can happen when a strong engineer, a strong codebase, a huge test suite, serious review loops, and frontier models are combined. It should not be read as permission for every founder to ask an agent to rewrite production code overnight.

The founder lesson is narrower and more useful:

Start small.
Preserve behavior.
Separate implementation from review.
Make tests and manual checks specific to the product.
Treat failed trial runs as signal.
Keep humans responsible for release decisions.

AI agents can make ambitious engineering work feel newly possible. That is exactly why the release process must become more explicit. Speed without a gate is not leverage. It is just a faster way to move risk into production.

References

The most dangerous AI coding demo is the one that works once.

Then the real product starts speaking.

This guide gives non-technical founders a practical operating rule:

Before an AI agent rewrites production code, make it pass a small trial run, an independent adversarial review, and a product-specific release gate.

That is still lighter than recovering from a broken launch. The goal is to separate safe assistance from unmanaged autonomy.

Why This Matters Now

The useful lesson is the opposite:

The rewrite became plausible only because the team wrapped the agents in process.

That makes the process more important, not less.

Start With a Change Class

Do not begin by asking whether the AI agent is good. Begin by classifying the change.

Use four classes:

Change class	Examples	Default rule
Cosmetic	Copy, spacing, icons, simple style changes	Agent can edit; human previews
Local functional	One form, one page, one API route, one email template	Agent can edit after a small test plan
Cross-cutting	Auth, billing, database models, permissions, shared components, analytics	Trial run and adversarial review required
Irreversible or high-trust	Deleting data, changing legal records, payments, medical, financial, identity, security, customer communications at scale	Do not ship without expert review and rollback plan

The class matters because AI agents make every task feel like a prompt away. That feeling is false. Changing a button label and changing how invoices are generated are not the same product decision.

Write the Behavior Contract Before Code

Before the agent edits anything, write a short behavior contract in plain language. This becomes the anchor for the trial run and review.

This is not a full technical specification. It is a list of promises the product already makes.

Run a Three-File Trial Before a Big Rewrite

The most transferable lesson from the Bun rewrite is the trial run.

Founders should copy the pattern even for much smaller products.

The trial run has three jobs:

Show whether the agent understands the codebase.
Reveal the types of mistakes it makes.
Improve the instructions before the expensive part begins.

Use this rule: If the trial run creates failures you cannot explain, stop the rewrite. You can still use the agent for smaller tasks. You are not ready for the large change.

Separate the Implementer From the Reviewer

This matters because the agent that wrote the code is biased toward completing the task. Humans have the same bias. An agent can explain its own work fluently and focus on proving completion.

For founder workflows, the pattern is simple:

Ask one agent to implement only the trial change.
Ask a second agent, in a fresh chat or separate tool session, to review only the diff against the behavior contract.
Ask the reviewer to assume the change is wrong until proven otherwise.
Ask for failure cases, security concerns, data-loss risks, missing tests, and user-facing regressions.
Do not let the reviewer apply fixes in the same pass.

Then give the reviewer findings back to the implementer.

That limitation is useful. Treat AI review as a bug-finding input, not as permission to merge.

Build a Product-Specific Gate

Founders often ask for "tests" as if tests are one thing. They are not. A useful gate combines automated checks, manual checks, and business-specific checks.

For an AI-built web app, use five layers.

1. Static and Build Checks

These are the basics:

The project installs cleanly.
Linting and type checks pass if the project has them.
The production build succeeds.
There are no new secret keys, test tokens, or local file paths in the diff.
The agent did not change deployment config unless that was explicitly requested.

These checks catch obvious damage. They do not prove the product works.

2. Behavior Contract Checks

Turn each behavior promise into a check. If the founder cannot automate it yet, write it as a manual release checklist.

For example:

Create a new account.
Log in as an existing user.
Trigger the webhook with valid and invalid signatures.
Upgrade, cancel, and reactivate a subscription in sandbox mode.
Export a document and compare the file name and owner.
Confirm a normal user cannot open the admin route.

The checklist should be boring; it prevents the founder from testing only the happy path shown in the demo.

3. Data Safety Checks

Any change touching persistence needs a data plan.

Ask:

What data can this change create, update, or delete?
Can the change be rolled back without losing user work?
Is there a backup before migration?
Does the new code handle old records?
Does it handle empty records, duplicate records, and partially migrated records?
Are background jobs idempotent if retried?

If you cannot answer these questions, do not let an agent run a production migration. Use a staging copy, export, or dry run first.

4. Observability Checks

A release gate should include how you will know the change failed.

For a small product, that may be simple:

Error dashboard checked after deploy.
Payment provider events checked after test purchase.
Support inbox watched for the first day.
Key workflow logs sampled.
Agent/tool traces reviewed if the product uses AI internally.

5. Rollback Checks

Before shipping, ask:

What exact command or platform action reverts the deploy?
Would reverting code also require reverting data?
Is there a feature flag or canary path?
Who is allowed to make the rollback decision?
What user message will we send if the failure is visible?

The rollback plan does not need to be dramatic. It needs to exist before the founder is under pressure.

Watch for AI-Specific Failure Modes

AI code changes fail in familiar software ways, but some patterns are more common when the author is an agent.

A Founder-Grade Prompt Pattern

Use this pattern before cross-cutting changes:

text

We are considering a production code change.

Goal:
[Describe the change in one paragraph.]

Behavior contract:
- [Promise 1]
- [Promise 2]
- [Promise 3]

Change class:
[Cosmetic / local functional / cross-cutting / irreversible or high-trust]

Trial run:
Change only these files or this low-risk workflow first:
- [file or workflow]

Constraints:
- Do not change deployment configuration.
- Do not change database schema unless explicitly requested.
- Do not remove existing tests.
- Do not skip failing tests.
- Explain any behavior change before making it.

Output:
1. Plan
2. Files likely to change
3. Risks
4. Trial-run implementation
5. Tests and manual checks

Then use a separate reviewer prompt:

text

You are reviewing this diff adversarially.

Assume the change is wrong until proven otherwise.
Compare it against the behavior contract.
Look for:
- user-facing regressions
- data loss or migration risk
- security or permission changes
- missing tests
- hidden cost increases
- changed behavior outside the stated scope

Do not fix the code. Return findings with severity and reproduction steps.

The second prompt is where many founders find the real value. It turns the agent from a builder into a skeptical reviewer.

When Not to Attempt the Rewrite

Some projects are not ready for agent-driven rewrites.

Do not attempt a large AI rewrite if:

You cannot describe the current product promises.
There is no staging environment or preview deploy.
The change touches payments, identity, health, legal, financial, or destructive data flows and no expert can review it.
The app has real users but no error monitoring.
The agent proposes deleting tests, skipping tests, or replacing a failing test with a weaker one.
The trial run changes files outside the agreed scope.
You cannot tell whether the trial run preserved behavior.
The business cannot tolerate a rollback window.

This is not anti-AI. It is pro-product.

The Real Lesson From the Bun Rewrite

The founder lesson is narrower and more useful:

Start small.
Preserve behavior.
Separate implementation from review.
Make tests and manual checks specific to the product.
Treat failed trial runs as signal.
Keep humans responsible for release decisions.