Rewriting a Core API Without Language Experts: A Six-Step AI-Assisted Migration Method

Many engineering teams face the same dilemma: a core service was selected years ago, its performance headroom is shrinking, and fewer engineers understand the legacy codebase. Everyone agrees that a rewrite is needed, but no one wants to own the risk.

A publicly discussed case is worth studying not because it proves that “AI can write code in a new language,” but because it offers a practical engineering answer: when intelligent coding tools produce much of the implementation, how do you prove that the result is correct, keep verification costs manageable, and migrate a core API without introducing new behavior?

Two Excuses the Case Challenged

The first excuse is: “We do not have experts in the new language, so we cannot start.”

When the platform engineering team rewrote its core API in a memory-safe language, it initially had no production experience with that language. The team did not ask AI to work independently. Human engineers owned the system design, boundaries, and acceptance criteria, while intelligent coding tools handled a large portion of the implementation.

The project lead emphasized that this was still fundamentally a distributed-systems project. Architecture decisions, error semantics, concurrency models, and observability could not be delegated to a code-generation tool.

“Zero experts” described the starting point, not the entire project. The team later added engineers with relevant experience, and knowledge transfer took place throughout the migration.

The second excuse is: “A rewrite is too risky, so we have to move the system one small piece at a time.”

The conventional approach is to divide a system into small components and migrate them over several years. That often creates a long project timeline and many parallel branches.

This case took a more aggressive path: the team built a complete candidate implementation, sent it shadow traffic, compared the outputs of the legacy and candidate systems, and then shifted real traffic gradually. This was not an uncontrolled rewrite. It was deliberately constrained as a pure port: no new features, no changes to business semantics, and controlled variables for more predictable verification.

The public interview provided rough estimates of a 40% to 50% reduction in CPU usage, a 10% to 20% improvement in time to first token, and larger gains for workloads with longer runtimes. These figures were verbal estimates rather than results from a formal benchmark, so they should be treated as field-level indications, not external performance commitments.

The most reusable lesson is not the numbers. It is the order of operations: prove correctness first, then discuss performance.

A Six-Step Migration and Verification Method

Step 1: Freeze the Migration Boundary and Acceptance Criteria

The easiest way for a migration to lose control is to rewrite the system while adding features at the same time.

Start by defining the boundary of a pure port. The contract should cover:

• API paths
• Request and response fields
• Status codes
• Error codes
• Sorting semantics
• Pagination behavior
• Idempotency requirements
• Timeout behavior
• Retry semantics

Acceptance criteria should include, at a minimum:

• Functional equivalence
• Error-path equivalence
• Performance baselines
• Complete observability
• Rollback capability

Performance measurements should distinguish CPU efficiency, memory usage, average latency, percentile latency, and long-running task behavior. Looking only at a single average can hide important regressions.

Step 2: Have Human Engineers Design the Target System First

AI can generate code, but it cannot replace architectural ownership.

Before implementation begins, the target system should have clearly defined:

• Module boundaries
• Domain models
• Concurrency models
• Dependency direction
• Error-handling strategies
• Logging, metrics, and tracing conventions
• Deployment topology
• Capacity model

Core APIs often involve authentication, rate limiting, idempotency, retries, timeouts, and contracts with upstream and downstream services. Any semantic change in these areas can create production differences.

The clearer the human-designed system is, the easier the generated implementation becomes to review and verify.

Step 3: Create an Implementation Plan and Generation Constraints

The implementation plan should define:

• The module inventory
• An approved dependency list
• Coding conventions
• Commit granularity
• Code-review rules
• Static-analysis rules
• The generation-and-review cycle

A coding tool should not generate code through unrestricted completion. It should produce implementation under the constraints of written specifications and interface contracts.

Static analysis is especially important during migration. It can help verify that the candidate implementation covers all entry points, error branches, and newly added functionality in the legacy system.

In the case discussed, other teams continued adding features to the old version. The migration team therefore needed static analysis and similar controls to ensure that those additions had corresponding implementations in the candidate system. Otherwise, the “pure port” could silently omit functionality.

Step 4: Build a Complete Candidate and Add Contract Tests

Instead of migrating isolated fragments and repeatedly stitching them together, build a candidate implementation for the complete API. This exposes interface contracts, error semantics, and upstream or downstream interactions earlier.

Testing should include, at least:

• Contract tests
• Boundary-condition tests
• Error-path tests
• Property-based tests
• Fuzz testing

Contract tests should be derived from the behavior of the legacy system rather than from its implementation details.

Property-based tests can verify invariants such as idempotency, ordering, and retry behavior. Fuzz testing can reveal inconsistencies in parsing and handling of malformed or unexpected input.

Generated code still requires human review. Reviewers should pay particular attention to resource release, concurrency safety, error propagation, and timeout handling.

Step 5: Run Shadow Traffic and Compare Outputs

After deployment, the candidate system should initially receive mirrored production requests without serving the real response. The legacy system continues responding to users, while the candidate records its results.

Before comparison, normalize the outputs. Ignore values such as request IDs, timestamps, floating-point noise, and unordered collections. Preserve status codes, error codes, business fields, ordering, and pagination semantics.

Differences should be classified rather than treated as one undifferentiated failure:

• Semantic differences
• Serialization differences
• Error-code differences
• Undefined-behavior differences
• Performance differences

A simplified comparison flow looks like this:

FUNCTION shadow_compare(request):
    baseline = call_legacy(request)
    candidate = call_candidate(request)
    baseline_norm = normalize(baseline)
    candidate_norm = normalize(candidate)
    IF baseline_norm != candidate_norm:
        record_diff(request.id, baseline_norm, candidate_norm)
    ELSE:
        record_match_metrics(request.id)

The shadow phase should also measure CPU usage, memory consumption, latency percentiles, long-running task duration, error rates, and resource saturation points.

Traffic shifting should begin only after functional differences have converged to a small and explainable set.

Step 6: Shift Traffic Gradually and Verify Rollback

Traffic should be increased incrementally by API, tenant, region, or percentage. Every stage should retain a rollback switch and comparison metrics.

During the rollout, continue sampling shadow traffic. This helps expose long-tail requests and low-probability error paths that may not appear in normal testing.

Functional alignment must continue in parallel. If the legacy system receives new features, those changes must be mapped to the candidate system until the migration is complete.

Before sending all traffic to the new implementation, run rollback drills, capacity tests, and fault-injection exercises. Confirm that monitoring and alerting can identify the specific request and the type of difference involved.

Three Lines of Defense for Verifying AI-Generated Code

The first line is specification and static checking. Specifications define inputs, outputs, error semantics, and invariants. Static analysis checks dependencies, entry-point coverage, and dangerous patterns.

The second line is contract testing and property-based testing. These tests do not care how the code is implemented. They verify whether its behavior conforms to the contract.

The third line is shadow execution and human review. Shadow traffic provides evidence from real production requests, while human reviewers determine whether differences are acceptable and whether they conceal deeper semantic problems.

All three lines are necessary:

• Code review alone cannot cover real production traffic.
• Shadow execution alone cannot explain the root cause of every difference.
• Testing alone may miss undefined behavior.

The team should formalize the difference-handling process. Every discrepancy needs a category, an owner, and either a fix or a documented reason for exemption. Any exemption should include its associated risk.

Organizational Model and Risk Boundaries

This type of project can be owned by a small team, but that team needs clearly assigned architecture and verification responsibilities.

The effort should not be treated as “automatic AI migration,” and the entire system should not be rewritten in one uncontrolled operation.

A pure port is a way to control variables, not a universal migration strategy. If business semantics must change, complete the pure port first and handle feature evolution as a separate effort.

The performance figures from the public case were rough verbal estimates, not formal benchmarks. “Zero experts” described the starting point, not the full project. Static analysis and parallel feature alignment are also areas where teams often underestimate the workload.

The central conclusion is simple: AI can reduce implementation costs, but it does not eliminate verification costs. Engineering teams must treat proving correctness as a first-class deliverable.

A reusable checklist includes:

1. Freeze the migration boundary.
2. Define the contract.
3. Design the target system with human ownership.
4. Use specification-driven generation.
5. Apply static analysis.
6. Run contract and property-based tests.
7. Execute shadow traffic.
8. Classify behavioral differences.
9. Shift traffic gradually.
10. Rehearse rollback and maintain parallel feature alignment.

When these practices are implemented rigorously, AI-assisted migration becomes more than a risky rewrite. It becomes an auditable, reversible, and repeatable engineering process.