The hard part of AI agents is not building them

The short answer

Building an agent that handles a task in a controlled test is now a matter of weeks. The hard part starts after: making it reliable on real cases, safe when it can act on systems, and still correct six months later when prices, rules and products have changed.

Executives who budget for the build and not for what follows end up with an agent that worked at launch and quietly degrades.

Reliability on real cases

Test cases are clean. Real cases arrive incomplete, contradictory and in formats nobody anticipated. An agent that resolves 90 percent of test cases may resolve far fewer of real ones, and the failures concentrate where the money is.

The work is building a reference set from real cases, measuring against it before every change, and deciding in advance which cases go to a person.

Safety when it can act

An agent that only answers can be wrong. An agent that changes a record, issues a refund or approves a request can be wrong expensively. Every action needs a limit, a log and a rule for when a human approves.

This is not a feature added at the end. It shapes the design from the first week, and it is what your risk and compliance teams will ask about.

Staying correct as the business changes

Policies change, products launch, a regulator issues a new rule. The agent does not know unless someone tells it. Without an owner and a routine for updates, accuracy erodes without any visible failure.

This is why an agent is an operation, not a purchase. Someone has to own it the way someone owns any process the business depends on.

What this means for budgets

Plan for three lines, not one: the build, the integration with your systems, and the monthly cost of operating and maintaining it. In our experience the second and third together usually exceed the first over a year.

A proposal that shows only the build is not cheaper. It has moved the cost to a point where you have less leverage to negotiate it.