Build an AI Agent That Survives Contact With Real Work

You have seen the video. An agent opens a browser, fills a form, cross-references three systems and reports back. It looks like the future arriving on schedule. Then you try it on your own process and it confidently books the wrong thing, or loops, or quietly gives up halfway. The gap between those two moments is the whole job, and it is the part almost nobody shows you.

Who this is actually for

Agent work pays off when the task genuinely needs judgement: reading messy documents, deciding which of several routes applies, pulling from tools in an order that depends on what it finds. Support triage, back-office processing, research assembly, operations coordination. Multi-step work where a fixed script would break the moment reality deviates.

It is the wrong purchase when your process is a fixed sequence. If a plain workflow or a function call handles it, adding reasoning buys you cost, latency and fresh failure modes for nothing. Knowing which side of that line you sit on is the first decision, and it is worth being honest about it before anyone writes code.

What changes when it is done well

A reliable agent feels less like a chatbot and more like a junior colleague with a narrow remit. It knows which tools it may touch and which it may not. It stops and asks before anything irreversible or expensive. It remembers what it already did, so it does not repeat itself. When a tool times out, it retries or falls back rather than inventing an answer.

The less visible half is the part your team should insist on. Traces that show what the agent did and why, so a bad outcome can be diagnosed instead of guessed at. Token and latency budgets so a runaway loop cannot quietly run up a bill overnight. A test suite covering the ugly inputs, not just the happy path. And permissions scoped tightly enough that a wrong decision is inconvenient rather than expensive.

What it takes

Expect the reliability work to outweigh the clever part. Getting an agent to do something once is a weekend. Making it do the same thing a thousand times, across flaky tools and inputs you did not anticipate, is where the effort goes. That means someone owns the evals, someone watches behaviour after launch, and there is a plan for drift as your data and tools change.

Ownership matters more than usual here. If the agent lives inside a vendor’s black box, you cannot tune it, audit it or move it. Code and configuration should sit with you, and the people who built it should be around for the first month of real traffic, because that is when the edge cases surface.

A good result is unglamorous: an agent that handles a defined slice of real work, fails safely when it should, and can be explained to an auditor. That is a far better outcome than an autonomous demo that impresses in a meeting and terrifies everyone in production.

The full guide sets out the build package in detail: how tools get scoped, where approval checkpoints belong, how evals and observability are structured, and the cost controls that keep an agent honest. Read AI agent development for UK businesses to see what a production-grade build actually contains.