Topic, page 5
Page 5 of articles on AI.
Without evals you cannot tell an improvement from a regression, so every change becomes an argument. That is a team problem before it is a quality one.
Most teams only ever write servers. Building the client side is what shows you where the protocol stops and your own orchestration has to take over.
Every agent framework needs to call your internal APIs, not just the web. A custom MCP server exposes them safely once, instead of bespoke glue per agent.
LangChain chains are a pipeline. LangGraph graphs are a state machine. The moment your agent needs to loop or branch on its own output, you've outgrown a chain.
The demo works on day one and degrades quietly for six months. Retrieval quality and failure behaviour decide whether RAG survives contact with real users.
Whether it needs designing from scratch or rescuing from its own success, tell me what you’re building and I’ll tell you how I’d architect it.
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