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Use an AI coding agent as a supervised engineering assistant, not as an autonomous rewrite button. The safe pattern is to select one bounded business flow, document what the existing system actually does, define the replacement design and acceptance tests, restrict the agent’s access, and merge only small changes that humans have reviewed and validated. Expand to another flow only after the pilot exposes its unknowns and the team has corrected its process.
A useful test for readiness is to ask: Is the code easy to change? Can you get nearly instantaneous feedback when you do change it? Do you understand it? If the answer is no, the first job is to create enough understanding and feedback—not to ask an agent to rewrite everything.
What “safe rebuild” means
A legacy rebuild is safe when the team can explain the behavior being changed, detect an unintended difference, and stop or reverse the work without damaging production data or business operations. An agent can accelerate inspection, documentation, test creation, and implementation, but it cannot take responsibility for business rules, architectural boundaries, access decisions, or release approval.
The practical objective is usually behavioral parity before improvement: for agreed inputs, the new implementation produces the same business-relevant outputs and side effects as the old one. Once that contract is demonstrated, the team can make deliberate improvements rather than accidentally changing rules while translating old code.
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OpenAI’s November 19, 2025 cookbook example, “Modernizing your Codebase with Codex,” illustrates this pattern with a COBOL investment-portfolio system. It is a workflow example, not independent evidence that any coding agent is safe or that modernization will succeed automatically. The same phases can be adapted to other languages and stacks.
Start with one business flow, not the whole repository
Choose a boundary you can observe
Pick a single process with a clear start and end: for example, a portfolio valuation batch, an invoice-import job, or one customer-account update path. The flow should be important enough to justify the effort but narrow enough that the team can enumerate its programs, jobs, data sources, dependencies, outputs, and operational schedule.
- In scope: the entry point, participating modules or jobs, data stores, external interfaces, generated files, and business outcomes.
- Out of scope: unrelated applications, broad framework upgrades, cosmetic refactors, and “cleanup” that is not required to prove the flow.
- Definition of done: documented behavior, target design, representative fixtures, parity results, reviewed code, security checks, and an approved release or rollback plan.
Write the rationale and sequence before the agent edits code. A vague instruction such as “rewrite the legacy system in a modern language” hides too many decisions and produces a diff that is difficult to review or validate.
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| Decision | What to record |
|---|---|
| Business flow | Trigger, actors or schedules, inputs, outputs, side effects, and owner. |
| Boundaries | Repository paths, services, tables, queues, files, and external systems included or excluded. |
| Constraints | Allowed languages, frameworks, deployment targets, performance limits, regulatory controls, and compatibility requirements. |
| Acceptance | Scenarios, expected results, equality or tolerance rules, and required review approvals. |
| Rollback | How to disable the new path, restore the previous version, and recover any changed data. |
Understand the existing system before designing its replacement
Ask the agent to help trace the selected flow, but treat its explanation as a hypothesis. A domain expert must compare it with source code, configuration, job schedules, production observations, and the knowledge held by operators and business users.
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Build a system overview
For the pilot, create a concise map that shows:
- Programs, modules, scripts, batch jobs, and orchestration order.
- Input files, tables, message topics, APIs, and other data sources.
- Transformations, calculations, validation rules, and branching conditions.
- Writes, emitted files, messages, reports, notifications, and other side effects.
- Dependencies on schedulers, credentials, environment variables, shared libraries, and external services.
- Failure handling, retries, reconciliation steps, and manual interventions.
Have the agent cite file paths, symbols, configuration keys, and call sites for each statement it makes. Mark every item as verified, inferred, or unknown. This prevents a plausible but incorrect generated summary from becoming the specification.
Find the behavior that the code does not make obvious
Legacy behavior often lives outside the main source file: a scheduler parameter, a fixed-width record layout, an undocumented rounding convention, an operator runbook, or a downstream report that depends on a particular sort order. Ask people who operate and consume the flow to identify these contracts. If the team cannot explain a rule, preserve the observed behavior first and open a separate decision for changing it.
Define the target design before implementation
Document the replacement while the current flow is still visible. The design should state which service or module owns each responsibility, how data is represented, and how callers invoke the new path.
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Specify ownership and interfaces
- Ownership: the service, module, or batch component responsible for each business rule and persistence operation.
- Data model: fields, types, required and optional values, identifiers, units, precision, time zones, retention, and migration treatment.
- Interfaces: public APIs, message contracts, command-line or batch entry points, response codes, error formats, and versioning policy.
- Operational behavior: scheduling, idempotency, retries, observability, alerting, and reconciliation.
- Compatibility: how old and new components coexist during rollout and how traffic or batches are switched back.
Do not let the agent silently invent an architecture while translating statements. Ask it to propose alternatives when a boundary is unclear, then have an architect and domain owner choose one and record the decision.
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Create parity tests before changing production code
“The tests pass” is not evidence of equivalence unless the tests exercise the behavior that matters. Build a validation plan before implementation and make the comparison rule explicit for every output.
Assemble representative fixtures
- Normal cases covering the common business path.
- Boundary values, empty and maximum-size inputs, unusual dates, currencies, and precision-sensitive numbers.
- Invalid, duplicate, late, partial, and out-of-order records.
- Known production incidents or manually reconciled cases, with sensitive data masked or synthesized.
- Cases that exercise retries, restarts, partial failures, and downstream side effects.
Store the input dataset and expected results under version control or an equivalent controlled system. Record which values must match exactly and which may differ within an approved tolerance. For money, counts, identifiers, statuses, and persisted records, exact comparison is usually appropriate; any tolerance must be justified by the domain owner rather than chosen for convenience.
Run old and new behavior side by side
- Prepare an identical, immutable fixture for both implementations.
- Run the legacy path in an isolated environment and capture outputs, side effects, logs, and timing relevant to the acceptance criteria.
- Run the candidate implementation with the same fixture and equivalent configuration.
- Normalize only differences that are explicitly non-semantic, such as generated timestamps or nondeterministic identifiers.
- Compare business outputs and side effects using the rules written in the validation plan.
- Investigate every mismatch; do not mask it by weakening the assertion or deleting the test.
- Preserve the run, configuration, code revision, and comparison result so another reviewer can reproduce it.
The modernization cookbook calls for shared datasets, side-by-side runs, and a definition of matching outputs. That approach turns parity from an opinion into an observable decision.
Give the agent narrow authority
Configure the environment so a mistaken instruction or generated command has a limited blast radius. CISA and international partners’ guidance on agentic artificial intelligence recommends limiting autonomy and avoiding broad access to sensitive data and critical systems, with layered defenses, identity management, and oversight.
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Set repository and tool boundaries
- Grant read access to the repository areas needed for the pilot and write access only to a dedicated branch or workspace.
- Use a sanitized dataset or a disposable environment; do not expose production secrets, unrestricted customer records, or critical credentials.
- Allow only the commands, package registries, test services, and file paths required for the task.
- Require a human approval step for permission changes, data migrations, deployment, destructive commands, and changes to authentication or authorization.
- Log prompts, tool calls, command output, diffs, approvals, and test results according to the organization’s retention and access policy.
- Use separate identities and least-privilege credentials for development, testing, staging, and production.
Sandboxing reduces risk but does not make generated code trustworthy. A compromised dependency, unsafe script, or incorrect business assumption can still pass through if review and validation are weak.
Use the agent for small, reviewable changes
Good bounded tasks
- Explain one module and list its callers, inputs, outputs, and side effects with file references.
- Draft characterization tests for an existing function without changing its behavior.
- Implement one adapter or transformation described in the approved design.
- Translate one routine while preserving names, validation order, rounding, and error behavior.
- Update documentation and fixtures when a reviewed behavior decision changes.
A repeatable change loop
- Give the agent the relevant plan section, constraints, files, and acceptance tests. State what it must not touch.
- Ask for a short implementation plan and likely risks before it edits anything.
- Review the plan for missing business rules, unsafe assumptions, and scope creep.
- Let the agent make one coherent change, then inspect the complete diff—not just the lines it describes.
- Run formatting, compilation, unit tests, integration or functional tests, static analysis, dependency checks, and security scans available to the project.
- Compare parity fixtures and inspect logs and side effects.
- Have a human reviewer approve, request changes, or reject the patch with a recorded reason.
- Merge only after the required checks and approvals succeed.
GitHub’s guidance on reviewing AI-generated code emphasizes automated functional checks and human oversight, particularly for legacy repositories and large pull requests. NIST’s AI-specific profile, SP 800-218A (July 2024), likewise provides secure-development practices that include code review and analysis. These controls reduce risk; they do not guarantee that every defect will be found.
Reject deceptive “green” builds
Do not accept a patch that removes failing tests, changes expected outputs without an approved business decision, broadens a fixture until it no longer represents reality, or catches and ignores errors merely to make a pipeline pass. A failure is evidence to investigate. If the intended behavior really changed, update the specification, approval record, fixtures, and implementation together.
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NIST SP 800-218A augments the Secure Software Development Framework for AI models and systems that use them. It is not a complete coding-agent migration playbook, but its secure review and analysis practices are useful controls for AI-assisted development. NIST’s broader SSDF describes fundamental secure software-development practices that can remain the baseline for the rebuilt system.
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Apply the same threat modeling and access review to generated code as to hand-written code. Check authentication and authorization paths, input validation, output encoding, secrets handling, dependency provenance, logging of sensitive values, error disclosure, and failure recovery. Review data-flow changes for privacy and retention consequences. An agent should not decide that a legacy permission check is obsolete simply because it is inconvenient to reproduce.
Know when to pause or roll back
Pause the pilot when
- The team cannot explain a material legacy behavior or identify its owner.
- Parity mismatches remain unresolved or are being hidden by weaker assertions.
- The target design crosses an unapproved data, security, or ownership boundary.
- The agent repeatedly edits outside scope, uses unauthorized tools, or produces unreviewable pull requests.
- Operational requirements such as reconciliation, restartability, or audit logging are not represented in tests.
Recover from a bad change
- Stop promotion and preserve the failing revision, logs, fixtures, and agent activity record.
- Determine whether the issue is a code defect, an incorrect parity assumption, bad test data, or an undocumented legacy rule.
- Revert or disable the candidate path using the preapproved mechanism; avoid emergency edits that destroy evidence.
- Protect and reconcile any data already written by the new path.
- Update the design, acceptance criteria, and runbook before asking the agent for another patch.
- Require a fresh review and complete validation cycle before resuming.
Expand only after the pilot teaches you something
At the end of the pilot, record unresolved behavior, data mismatches, security findings, operational surprises, review effort, and changes to the plan. Decide whether the next flow has similar boundaries and validation coverage. A successful pilot demonstrates that the team can control this pattern for one flow; it does not prove that every application, language, or migration type is safe to automate.
How to evaluate coding agents without ranking vendors
The available evidence does not establish a best agent for a particular legacy language, domain, or migration. Compare tools against your own controls and workflow rather than headline model claims.
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| Evaluation axis | Questions to ask |
|---|---|
| Repository context and change visibility | Can the tool show the files, symbols, assumptions, and complete diff it used? |
| Permission controls | Can you restrict paths, commands, network access, credentials, and data, and require approval for high-impact actions? |
| Validation integration | Can it run the project’s build, functional tests, parity fixtures, static analysis, and security checks without bypassing them? |
| Auditability | Are prompts, tool calls, outputs, approvals, revisions, and test evidence retained for review? |
| Team fit | Does the workflow fit existing branching, pull-request, incident, release, and compliance practices? |
Run a controlled proof of concept on a non-sensitive slice and measure reviewability and evidence quality, not just lines of code produced. The sources do not provide vendor-neutral head-to-head performance or safety results for legacy rebuilds.
Further reading
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