AI has dramatically reduced the cost of writing code. It has not reduced the cost of owning it.
That distinction may become one of the most important architecture lessons of the AI era.
Today, almost anyone inside an engineering organization can open an AI coding assistant and say:
"Here is my API. Build an MCP server for it."
If your tool server needs to interrupt the model to ask a question, ask why the question wasn't answerable before the call started.
MCP elicitation is a real, spec-defined capability, and it solves a real problem. It's also becoming a convenient place to hide a design problem instead of fixing it.
Your API is already production-hardened. It has authentication, validation, rate limits, monitoring, and a battle-tested OpenAPI spec describing every operation. None of that changes when an AI agent becomes the caller instead of a browser or a mobile app.
The most valuable MCP server may be the one that refuses to become your application.
MCP is moving toward a clearer architectural truth: servers should be stateless.
Every line of business logic you duplicate into an MCP creates another place where your system can eventually disagree with itself.
And that disagreement has a cost.
Every few months, our industry declares the next abstraction layer "the new HTTP."
First it was SOAP.
Then REST. Then GraphQL. Then gRPC.
Now, it's MCP.
Today, we crossed a major milestone.
👉 The HAPI MCP Registry is now available in the ChatGPT marketplace, officially aligning with the ecosystem driven by OpenAI.
And this is bigger than a feature release.
This is a shift in how APIs and AI finally connect.
"We already solved this problem... and somehow we forgot."
Back in the day, database engineers learned this lesson the hard way.
We didn't call it "AI cost optimization." We called it bad query design.
And it hurt.
Enterprise AI is entering a new phase. Not the hype phase. Not the experimentation phase. The operational phase — where organizations must make AI safe, governed, and useful for real teams.
Over the last year, a clear pattern has emerged inside large enterprises experimenting with AI automation. What starts as scattered experimentation quickly evolves into a structured platform strategy.
Something subtle but massive just happened in developer tooling. The IDE Is No Longer the Center of Development — Agent Orchestration Is.
For decades, the IDE was the center of software development. Everything revolved around it: edit → run → debug → commit.
Now something else is emerging.
A control plane for AI agents.
