Designing AI Agents That Do Real Work

“Agent” has become one of the most overloaded words in AI. For this piece I mean something specific: a system that takes a goal, decides on steps, uses tools to act, observes the result, and repeats until it’s done. That loop is where the value is — and where most of the difficulty hides.

The loop is the product

A model that only talks is a feature. A model that can act — call a function, query a database, file a ticket — and then react to what happened is an agent. The quality of an agent is mostly the quality of this loop.

Tools are the real interface

People obsess over the prompt. In practice, an agent’s capabilities are defined by the tools you give it and how well they’re described.

• Give it few, well-named tools with clear inputs and outputs.
• Make tools hard to misuse — validate, constrain, return useful errors.
• Treat a tool call like an API contract, because that’s what it is.

Bound the autonomy

Unbounded agents are a great way to discover expensive failure modes. The agents that survive contact with production are tightly bounded:

1. A step budget, so it can’t loop forever.
2. A clear definition of “done” it can check against.
3. Human approval gates on anything irreversible.
4. Logging of every step, so you can audit and improve.

Start narrow

The best first agent is embarrassingly small: one job, a couple of tools, a human reviewing the output. Get that loop trustworthy, then widen it. An agent that reliably does one real task beats a general one that impresses in a demo and can’t be trusted on Monday.

Agents are not magic, and they’re not a buzzword to wave at every problem. They’re a design pattern — a loop, some tools, and a set of guardrails. Get those three right and they’ll quietly take real work off your team.