The first architecture reached production.
Growth warranted new boundaries.
I built the original monolith that brought a new platform into production. As the feature set grew and requirements were added, a move to microservices became warranted. This case study covers one focused extraction from that larger architectural evolution.
- 8Days to complete
- 14Production endpoints
- 80%Time reduction
- 6 wkTraditional estimate
The software changed because the product changed.
In a prior role, I built the monolith that brought a new platform into production. That architecture served the product at its original stage. The feature set then grew, new requirements were added, and parts of the system needed different boundaries. A move toward microservices became warranted by the product's evolution rather than by a blanket rule against monoliths.
One legacy API inside the monolith became a well-defined extraction target. It touched shared database tables, depended implicitly on adjacent services, and served downstream consumers whose contracts had to remain intact. The traditional estimate for working through its fourteen production endpoints was six weeks.
This project predated Pelican. It involved production code, a production test suite, and downstream consumers that would expose a contract regression. Completion required passing tests and validated API contracts.
A focused extraction, sequenced endpoint by endpoint.
I treated each endpoint as a controlled unit of migration. Before implementation, I documented what the endpoint did, its dependencies, the behavior its tests needed to cover, and the downstream contract that had to remain valid. That plan established the boundary for the change.
I used Claude Code within a manually coordinated workflow while retaining each completion decision. I reviewed the implementation, ran the tests, and validated its contract against downstream consumers. After those checks passed, I moved to the next endpoint.
This ran fourteen times.
Eight days.
Tests passing.
The fourteen-endpoint extraction completed in eight days against the six-week estimate. The endpoints moved into a containerized microservice with the test suite passing and API contracts validated against downstream consumers.
The same five phases, applied to each of fourteen endpoints.
The workflow made the extraction inspectable. Each endpoint moved through an explicit sequence, and each required my approval before the next phase began. The visible order below is the same order followed in the work.
Plan
I wrote a plan for the endpoint being extracted before any code was written: what it did, what it depended on, what the tests needed to cover, and what success looked like. The plan was saved to disk and required my approval before implementation started.
Implement
Claude Code implemented the endpoint against the approved plan. I reviewed the output before any tests ran. Implementation did not start without an approved plan, and no output moved forward without a review.
Test
The test suite ran after implementation. If tests failed, the implementation loop continued until they passed. The criterion for moving forward was a passing test suite, not code that looked correct.
Validate
I validated the API contracts against the downstream consumers. A passing test suite and a validated contract were both required before an endpoint was considered complete.
Next endpoint
Once both criteria were met, I moved to the next endpoint and repeated the process from the plan phase. This ran fourteen times, in sequence, with the same discipline applied to each.
Durable software changes with the product.
The original monolith and the later microservices were decisions made at different stages of the same product. Reaching production did not make the first architecture permanent. Product growth and added requirements changed what the system needed from its boundaries.
The extraction succeeded because the architectural work had a clear boundary. Planning, implementation, testing, and contract validation remained distinct for each endpoint. That structure made it possible to move quickly while preserving the evidence needed to decide when an endpoint was complete.
AI-supported implementation increased the pace, while review and go or no-go decisions remained senior engineering responsibilities. The eight-day result matters because the production checks remained part of the work.
The broader lesson is that the choice between a monolith and microservices follows the product's current responsibilities. Durable architecture also provides a deliberate path for change when those responsibilities grow.
Current workflow How the orchestration system works todayImportant software can
evolve deliberately.
If an existing system has become difficult to change, we can identify a focused first project and the evidence it needs before implementation begins.