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The safe way to use an AI coding agent on a legacy system is to treat it as a fast, supervised engineering assistant—not as an autonomous rewrite button. Start with one bounded business flow. Document what the existing system actually does, define the replacement design and measurable parity tests, restrict the agent’s access, and merge small changes only after people review the evidence. Expand to another flow only after the pilot exposes and resolves its unknowns.
This approach lets an agent accelerate code archaeology, documentation, test creation and routine implementation while engineers retain decisions about business rules, architecture, security boundaries and release risk.
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What a safe AI-assisted rebuild actually means
A rebuild is safe when you can explain the behavior you are changing, show how the new implementation is expected to behave, limit what the agent can touch, and recover if the result is wrong. It does not mean that an agent is secure by default or that a green test suite proves equivalence.
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Use four controls throughout the work:
- Bounded scope: one business flow with a clear start, end and owner.
- Observable behavior: representative inputs, expected outputs and relevant side effects captured before implementation.
- Constrained authority: repository, command, data and deployment permissions limited to what the current task needs.
- Reviewable evidence: small diffs, functional checks, security analysis and a human decision at each consequential transition.
The OpenAI cookbook example Modernizing your Codebase with Codex, published November 19, 2025, demonstrates this pattern with a COBOL investment-portfolio system. Its phases can be adapted to other languages and runtimes, but the example is a workflow description, not proof that any particular agent or modernization project is safe.
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Choose a pilot instead of asking for a rewrite
Pick one business flow
Select a process that has a recognizable boundary and enough evidence to validate: for example, calculating a portfolio valuation, producing a statement, accepting an order or importing a daily file. The flow should have identifiable entry points, data sources and outputs. Avoid a request such as “rewrite the whole system”; it gives the agent no defensible stopping point and gives reviewers no manageable unit of work.
Prefer a flow whose failure can be contained and whose domain owner is available to answer questions. A well-chosen pilot is not necessarily the smallest module. It is the smallest slice that teaches the team about dependencies, business rules, data quality and deployment constraints.
Write a scope and definition of done
Before opening an agent session, record:
- the business outcome and why this flow matters;
- the programs, jobs, services, data stores and external interfaces included;
- explicit exclusions, such as unrelated reports or a future data migration;
- the sequence of discovery, design, implementation and validation steps;
- acceptance criteria, including parity conditions and operational checks; and
- the person who can approve business behavior and the person who can approve release.
| Artifact | What it should contain | Approval |
|---|---|---|
| Flow brief | Scope, rationale, entry and exit points, exclusions | Product or domain owner |
| System map | Programs, orchestration, data sources, dependencies and schedules | Engineer familiar with production operations |
| Target design | Owning module or service, data model, interfaces and batch entry points | Architecture and domain reviewers |
| Parity plan | Fixtures, expected results, side effects and comparison rules | Domain owner and test owner |
| Change plan | Small tasks, permitted access, checks and rollback decision | Technical lead |
Understand the legacy behavior before designing its replacement
Inventory the execution path
Trace the selected flow from its trigger to its final output. List source programs, schedulers, shell scripts, copybooks or schemas, database tables, flat files, queues, third-party calls, environment variables and operational runbooks. Record which step transforms each important field and where errors are handled, retried or silently ignored.
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Use the agent for discovery, then verify it
Give the agent read-only access to the relevant paths and ask it to cite the files, symbols and configuration that support each conclusion. Useful bounded requests include explaining a module, tracing a field from input to output, listing callers of a routine, or drafting a dependency diagram. Treat every generated explanation as a hypothesis until an engineer checks it against the implementation and production knowledge.
Ask the team questions that expose readiness: Is your code easy to change? Can you get nearly instantaneous feedback when you do change it? Do you understand it? A “no” is not a reason to hand over more authority; it is a signal to improve observability, documentation or test harnesses first.
Capture behavior, including the awkward parts
Record normal cases, boundary values, malformed inputs, missing records, duplicate messages, time-zone and currency rules, rounding, ordering and failure recovery. Note behavior that looks accidental but is relied upon by another system. A replacement that is cleaner yet changes a relied-upon quirk is a behavior change and must be approved as such.
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Define the replacement’s ownership boundary in writing. State which service or module owns the flow, which data it owns, and which information remains in another system. Describe the target data model, identifiers, validation rules and migration assumptions. Specify public APIs, events, file formats or batch entry points, including versioning and error contracts.
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Keep business decisions separate from code generation. An agent can propose a decomposition or translate a known pattern, but a domain and architecture reviewer must decide whether that decomposition preserves invariants, regulatory rules and operational responsibilities. Record significant choices in short decision records so later prompts do not quietly reverse them.
Do not design from an agent’s summary alone. Compare its proposal with the inventory, ask it to identify unknowns and contradictions, and resolve those questions before implementation. If ownership or data semantics remain unclear, narrow the pilot rather than guessing.
Define behavioral parity as a testable contract
Build fixtures before writing the replacement
Choose shared input datasets that represent common, edge and failure cases. For each fixture, specify the expected outputs and side effects: returned values, persisted rows, emitted messages, generated files, status codes, audit records and alerts. Decide whether each field requires exact equality or an approved tolerance, such as a documented numeric rounding rule.
| Check | Example question | Evidence to retain |
|---|---|---|
| Input coverage | Does the set include valid, boundary, missing and malformed records? | Versioned fixture and case description |
| Primary output | What value, file or response should be produced? | Expected result and comparison rule |
| Side effects | Which rows, messages, files or audit events should change? | Before/after snapshot or event capture |
| Failure behavior | What error, retry or status is expected? | Observed error and approved expectation |
| Operational behavior | What logs, metrics or reconciliation records are required? | Run record and reviewer sign-off |
Run old and new implementations against the same inputs
Where feasible, execute the legacy and replacement paths side by side using identical fixtures and a controlled environment. Compare outputs with the rule defined for each field, then investigate every mismatch. A difference may reveal an agent defect, an undocumented legacy rule, a data-quality issue or an intentional design change. Classify it; do not hide it by weakening the test.
“The tests pass” is meaningful only when the tests exercise the behavior that matters. A narrow unit suite can pass while a scheduler contract, rounding rule or downstream file format is broken. Preserve the comparison results so a reviewer can see what was tested, what differed and who approved any intentional difference.
Restrict the agent’s authority
Joint guidance from CISA and international partners on adopting agentic AI services recommends limiting autonomy and avoiding broad access to sensitive data and critical systems. Apply that principle to every coding session, even when the repository appears low risk.
| Control area | Safer default | Require explicit approval for |
|---|---|---|
| Repository paths | Read access broadly enough to understand the pilot; write access only to its working area | Changes outside the approved scope or to shared infrastructure |
| Commands | Allow inspection, formatting and targeted test commands | Destructive operations, schema changes or scripts that alter production-like data |
| Secrets and data | Use synthetic or redacted fixtures and injected short-lived credentials | Access to production records, private keys, customer data or unrestricted secret stores |
| Network | Sandbox outbound access and log requests | Connections to production services or unapproved external endpoints |
| Merge and release | Agent may prepare a branch or pull request | Merging, permission changes, deployment, rollback or database migration |
Use separate identities for the agent, developer and deployment system. Log prompts, tool calls, file changes and test results according to your organization’s retention policy. Layer access controls with code review, environment isolation, monitoring and a tested recovery path; no single control compensates for an overprivileged session.
Work in small, evidence-producing increments
1. Give the agent a precise context package
Include the flow brief, relevant paths, invariants, target design, parity fixtures, forbidden areas and the checks it must run. Tell it to state assumptions and unresolved questions instead of filling gaps silently.
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Task: implement one approved step of the portfolio-valuation flow. Read: /src/valuation and /tests/fixtures/valuation. Do not modify: deployment, authentication, schemas, or unrelated modules. Preserve: rounding rule R-17 and error code E-204. Before editing, list files, assumptions, and tests you will run. Return a summary and exact diff.Adapt the paths and rules to your repository. Keep variable or path syntax inside code blocks so the instruction remains unambiguous.
2. Ask for explanation and a plan first
Have the agent identify the current behavior, affected files, proposed change, risks and validation steps before it writes code. Review that plan against the system map. If it discovers an unrecorded dependency, update the map and adjust the scope.
3. Implement one coherent change
Examples include adding a characterization test, extracting one pure calculation, translating one routine behind an existing interface, or adding an adapter for one data source. Avoid combining a refactor, schema change and feature change in one pull request; reviewers cannot tell which difference caused a mismatch.
4. Run checks immediately
Require the agent to run the narrowest relevant tests, then the build, integration or functional checks required by the project. Capture failures rather than asking it to remove or relax assertions. A failing test is information about the implementation or the parity contract.
5. Review the diff as consequential code
Read every changed line, generated migration, configuration edit and dependency update. Check control flow, error handling, data exposure, logging, resource use and compatibility with the legacy caller. Ask the agent to explain suspicious code, but make the acceptance decision yourself.
6. Merge only after independent checks
Use a second reviewer for business-critical behavior. Run available static analysis, dependency and secret scanning, and the parity fixtures. Record the reviewer, checks, unresolved findings and disposition in the pull request.
Apply secure-development and code-review practices
GitHub’s guidance on reviewing AI-generated code emphasizes automated functional checks and human oversight, especially for legacy repositories and large changes. Treat generated code under the same review policy as code written entirely by a person; provenance changes how you inspect it, not the standard it must meet.
NIST SP 800-218A, published in July 2024, is an AI-specific community profile that augments the Secure Software Development Framework. It is not a complete coding-agent migration playbook, but its recommendations for secure code review and analysis fit AI-assisted work. The broader NIST SSDF provides the foundational practices: define secure requirements, protect the software and development environment, produce well-secured software and respond to vulnerabilities.
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- Check authorization and input validation at every new boundary.
- Confirm that logs do not expose credentials or sensitive records.
- Review dependency provenance, licenses and transitive changes.
- Run static analysis and security tests on both the new module and its adapters.
- Verify that error paths fail closed where appropriate and cannot bypass audit or approval controls.
- Reject any change that deletes a failing test or alters an expected result without a documented business decision.
Release progressively and learn from the pilot
Keep the legacy path available until the replacement has demonstrated the agreed scenarios and operational checks. Use side-by-side or shadow execution where the architecture allows it, compare results, and define who can stop the rollout. Prepare a rollback that restores the previous implementation and data-consistency procedure; do not assume reverting source code alone reverses an irreversible migration.
Maintain a pilot log containing behavior mismatches, data anomalies, security findings, performance observations, decisions and open questions. Review it with the domain owner and operators before expanding scope. A successful pilot demonstrates that the team can repeat the controls for a similar flow; it does not prove that every other process is safe to migrate.
How to evaluate coding agents without pretending there is a universal winner
Available evidence does not establish which agent is best for a particular legacy language, domain or migration type. Evaluate candidates against your own controls rather than marketing claims.
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|---|---|
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| Permission controls | Can you limit paths, commands, network access, secrets and environments? Are approvals required for consequential actions? |
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| Auditability | Are prompts, tool actions, diffs, test results and identity recorded for review? |
| Team fit | Does its output fit your branching, pull-request, review, incident and rollback processes? |
Run a controlled evaluation on a non-sensitive slice using the same fixtures and review rubric. Compare the clarity of plans and diffs, not just the amount of code produced. Do not infer safety or productivity from an anecdotal demonstration.
Common failure modes and their fixes
“Rewrite everything” prompts
Why it fails: the agent cannot establish boundaries or preserve undocumented behavior across an entire estate.
Fix: return to one flow, define exclusions and require a system map and parity plan before code changes.
Trusting generated documentation
Why it fails: comments and diagrams can describe intended behavior while the running system follows a different path.
Fix: have an engineer verify each important claim against code, configuration, logs and operator knowledge.
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Using only happy-path tests
Why it fails: rounding, malformed records, retries and side effects often contain the real business contract.
Fix: add boundary, failure, duplicate, ordering and recovery fixtures before implementation.
Giving production access for convenience
Why it fails: an incorrect command or prompt-injection path can expose data or alter a critical system.
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Fix: use least privilege, isolated environments, redacted data, short-lived credentials and approval gates.
Accepting a huge pull request
Why it fails: reviewers cannot attribute a mismatch or security issue to one change.
Fix: split discovery, tests, refactors, adapters and behavior changes into coherent, independently validated increments.
Expanding after a superficially green pilot
Why it fails: passing tests may omit operational behavior, and one flow may not represent another.
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Further reading on changing legacy code
Michael Feathers’s Working Effectively with Legacy Code (Pearson paperback, ISBN 9780131177055) explains characterization tests, test harnesses and techniques for protecting against regressions in untested systems. Published in 2004, it is foundational legacy-code guidance rather than a book about AI agents, but its discipline of creating safety nets before refactoring applies directly to an AI-assisted rebuild.
For the governance layer, consult GitHub’s documentation on reviewing AI-generated code, NIST SP 800-218A and the NIST Secure Software Development Framework. Read the CISA and international-partner guidance on careful adoption of agentic AI services for recommendations about autonomy, sensitive-system access, layered defenses, identity management and oversight.
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