Most AI demos are impressive exactly once. You type a clever prompt, the agent does something magical, everyone claps, and then it quietly falls apart the moment a real user does something unexpected. The gap between a demo and a system you can put in front of people is the whole job.
Here’s how I think about closing that gap.
Retrieval is a data problem, not a prompt problem
Retrieval-augmented generation lives or dies on what you put in the index, not on how you phrase the question. Before touching a vector database, I spend time on chunking, metadata, and freshness - because a model can only be as good as what you hand it.
- Chunk around meaning, not character counts. A chunk should be a self-contained idea.
- Attach metadata you can filter on later (source, timestamp, permissions). Retrieval without filters leaks and drifts.
- Decide how stale is too stale, and build re-indexing before you need it.
Vector search is necessary, not sufficient
Similarity search gets you candidates. It doesn’t get you correctness. I almost always pair it with a second pass - re-ranking, a keyword filter, or a rules check - because the nearest vector is not always the right answer. Treat the vector store as a fast recall layer, then earn precision on top of it.
Agents need boundaries, not freedom
An agent with unlimited tools and no guardrails is a liability. The reliable ones look boring on purpose:
- A small, well-defined set of tools, each with a strict contract.
- Explicit stopping conditions so the loop can’t run forever.
- Validation on every tool output before it re-enters the model’s context.
- A clear trace of what happened, so failures are debuggable instead of mysterious.
When I expose capabilities to an agent - increasingly through an MCP server - I design those tools the way I’d design any public API: typed inputs, predictable errors, and no surprises.
Assume it will fail, then design for it
The difference between a toy and a product is what happens on the bad path. Timeouts, empty retrievals, malformed model output, a tool that 500s - all of these are normal, and all of them need a defined behavior. I’d rather ship an agent that says “I couldn’t do that” honestly than one that confidently makes something up.
That’s the bar I hold: not “look what it can do,” but “you can depend on this when it matters.”