Shipping AI Agents That Actually Do the Work

'Agent' might be the most overloaded word in tech right now. Strip away the hype and an agentic system is simple: an LLM in a loop. It observes the state, decides on an action, calls a tool, reads the result, and repeats — until the task is done or it decides to give up.

That's the whole trick. The intelligence you feel doesn't live in some special model; it lives in the loop, the tools you expose, and the rails you put around them. Get those right and a mid-tier model looks brilliant. Get them wrong and the smartest model on the market flails.

Tools are the actual product

An agent is only as capable as the tools you hand it. A model with a vague `search()` tool and no way to recover from a bad call will spin in circles. A model with a well-named tool, typed arguments and error messages that explain how to fix the mistake will quietly do the right thing.

Design tools the way you'd design an API for a sharp but literal junior developer. Small, composable, hard to misuse. Return errors the model can act on — 'no results for that date range, try widening it' beats a raw 500 every time.

Guardrails, not vibes

Autonomy is exciting until an agent decides to delete the wrong table at 2am. Bound the loop before you ship it: a maximum number of steps, a cost ceiling, an allow-list of tools, and a dry-run mode for anything destructive.

Treat every action the agent can take as something a stranger might trigger, because prompt injection means it effectively can. Least privilege is not optional here — it's the difference between a helpful assistant and an incident report.

Know when to stop

The best agents are the ones that recognise when they're stuck and ask for help instead of confidently inventing an answer. A wrong answer delivered with total confidence is far more expensive than an honest 'I couldn't figure this out.'

Build the escape hatch on purpose: a clear path back to a human, with the context of what was tried and why it stalled.

The takeaway

Ship the boring version first. An agent that reliably does one narrow thing beats a demo that attempts ten and botches most of them. Add autonomy the way you'd add any dangerous power tool — slowly, with the guard on.