Most AI agent MVPs start with the wrong question:
How much can we make autonomous?
A better first question is:
What is the smallest useful outcome a human can verify?
The difference matters. An autonomous demo can look impressive while hiding unreliable decisions, unclear permissions, and failure states that nobody has tested. A narrow, reviewable workflow is less dramatic, but it can become a real product.
Split the workflow into three kinds of work:
This boundary tells you where an LLM is useful and where ordinary code is safer. It also prevents the agent from quietly gaining permissions just because a demo needs to look seamless.
An agent tool should not be described as “search the system” or “update the record.” Define its inputs, outputs, timeouts, permission checks, and failure responses.
For example, a lead-research tool can return: The model can then reason over evidence instead of inventing a successful result. Typed contracts also make tool calls testable without invoking the full agent.
Five evaluation cases are usually more valuable than five more tools:
Each case needs an observable pass/fail rule. “The answer looks good” is not a rule. “The agent cites the source, marks the missing field, and does not call the write tool” is.
For an early release, prefer read-only tools. Let the agent prepare a draft, proposed database change, or command plan, then require a human to approve the final external action. This is not a permanent limitation. It is how you collect evidence about where the system is reliable enough to automate next.
The goal of an agent MVP is not to imitate a fully autonomous employee. It is to prove that one workflow can produce repeatable value without hiding uncertainty.
I turned this sequence into a reusable, editor-verified workflow on Codez Win:
What is the smallest agent workflow you have seen deliver repeatable value in production?