When to Use an AI Agent (and When Not To)

“AI agent” is the phrase of the moment, and for good reason: given a goal and the right tools, an agent can carry out real, multi-step work on its own. But that flexibility is also where projects go wrong: an agent pointed at the wrong problem is slower, costlier and less reliable than a plain piece of software.

Before you reach for an agent, it's worth being honest about what you actually need.

When an agent is the right call

Agents shine when the work is genuinely open-ended: the steps change from case to case, the inputs are messy, and a human would normally have to read, decide and act. Triage, research, drafting and routing across several tools are all good fits.

A quick test

If you can't write the rules down as a flowchart without it sprawling into dozens of branches, an agent that can reason through each case will usually beat trying to script every path.

When a simpler tool wins

If the task is the same every time (move this field there, send that email when this happens), you don't need a model in the loop. A small automation is faster, cheaper and far easier to trust. Reaching for an agent here just adds cost and a point of failure.

The honest answer is usually a mix: a thin layer of automation for the predictable parts, and an agent only where judgement is actually required. That's the line we help our clients draw before a single line of code is written.