An agent is software that decides its next step. That is what makes it useful, and what makes it dangerous when nobody drew the lines.
What an agent actually is
Classic automation follows a script: if this field says X, do Y. It breaks the moment reality leaves the script. An agent gets a goal instead. It reads the ticket, decides it needs the customer record, calls the CRM, checks the refund policy, drafts an answer and proposes an action. Every step is a decision the model makes, using tools you give it.
That is the whole trick, and the whole risk. Models are good at judgment calls on messy input. They are not good at knowing when to stop. So the engineering work is not making the agent smarter. It is deciding which tools it may touch, which actions need a human, what it may spend, and what happens when it is unsure.
Where agents pay off
The best candidates are workflows with volume, variety and a clear definition of done: support triage, claims intake, sales-ops cleanup, first response to incidents, document-heavy back offices. Work where a skilled person spends most of the day collecting context from five systems before making a decision that takes thirty seconds.
The payoff is rarely a headcount story. It is time. The agent does the collecting, the person does the deciding. Done well, the agent handles the routine share end to end and prepares the rest so thoroughly that the human decision gets faster too.
The engineering work is not making the agent smarter. It is drawing the lines it must not cross.
Where agents go off the rails
Agents fail in predictable ways. They get too many permissions because it was faster to set up. They read a document that contains instructions and follow them, which is indirect prompt injection. They loop, retry and spend. They take an action that is technically allowed and practically disastrous, like refunding the wrong customer, because nobody defined a limit.
None of this is exotic. It is what happens when an agent is built like a demo and run like a product.
How we keep them on rails
We start from the workflow, not the model. First we map what a person does today and mark every action with real-world consequences. Those actions get a permission, a limit or a human approval gate. The agent gets an allow-list of tools and nothing else, a spend cap, rate limits and a kill switch someone has actually tested.
Then we test it the way we would attack it: edge cases, odd inputs, poisoned documents. Every run leaves a trace you can read afterwards: what it saw, what it decided, which tool it called, who approved. Rollout is gradual, one queue or one team at a time, with an owner on your side who can say stop.