There is a point in every systems product where the architecture stops being a diagram and starts being a conversation.

That is what happened with LineageLens. At first, the problem looked straightforward: capture AI-generated code changes, link them to prompts and sessions, and store the result. The early mental model was centered on the proxy, the parser, and the provenance record. That is the part most people notice first.

But once the product had a VS Code extension, a FastAPI backend, and an MCP server, a deeper issue surfaced. The hardest problem was no longer “can we capture the event?” It was “can every layer describe that event consistently?”

That question matters more than it sounds.

A provenance tool is not just a recorder. It is a system of record. And a system of record only works if all its surfaces agree on what the record means. If the backend redirects a fresh user to setup while the client expects a login response, the user experiences confusion instead of flow. If the backend returns a workspace object in one shape but the MCP client formats another shape, the assistant layer becomes unreliable. If a Lite-only feature gate is converted into a generic authentication message, the error is technically handled but semantically wrong.

That is contract drift.

Contract drift is especially dangerous in AI infrastructure because the user is already operating in a high-uncertainty environment. They are trying to understand what a model did, what a human accepted, what the system stored, and what can be trusted later. If the product itself emits inconsistent signals, it compounds the uncertainty rather than reducing it.

The recent fixes in LineageLens were all aimed at that problem. Fresh installs now get the correct auth behavior instead of an opaque setup redirect. The MCP server now reflects the real workspace response shape returned by the backend. Lite-only denials surface the backend’s actual upgrade message. Ingest warnings and stored-state issues are shown instead of disappearing. Refresh-token handling makes the auth lifecycle more resilient. Small changes, but they all point in the same direction: the product should say the same thing everywhere.

That is the part I think is easy to underestimate when building tools like this. People often focus on the novel core, like parsing native tool calls or correlating edits to tool results. Those are important, but they are only useful if the surrounding surfaces stay honest. A provenance engine with broken boundaries is still fragile. A provenance engine with stable boundaries starts becoming something teams can actually rely on.

The architectural lesson is simple: once a product spans multiple surfaces, the boundary becomes the product. You do not just need the backend to work. You need the extension, backend, and MCP server to agree on the same state, the same error, and the same meaning.

For LineageLens, that is the difference between a tool that captures history and a system people can trust.