Look for a task that happens often enough to matter. Notice where people collect information, move it between tools or wait for context before they can act. The goal is to make that specific moment work better.

Why Most AI Agents Disappoint

It's Rarely the Model

Teams assume a weak agent means a weak model. In practice the model is fine — it simply doesn't know who is asking, what the company already promised, or which answer would cause a problem tomorrow. Capability without context produces confident nonsense.

Automation Without Understanding

An agent dropped onto a process nobody has mapped will automate the mess that's already there. Faster handoffs don't help if the handoff itself was the broken part. The work has to be understood before it can be handed over.

Understand the Work First

Before choosing an agent, map the current flow. What starts the task? Which information is needed? Where does the result go? Speak with the people who do the work and keep their exceptions in view — exceptions are where automation usually breaks.
A simple map makes the handoffs visible. It also helps you decide which parts can be automated and where a person should stay involved.

Context Is the Real Input

Connect It to Where Answers Live

A well-written prompt is not context. Real context is the CRM record, the order history, the policy page that was updated last week. An agent that can reach those sources answers from facts; one that can't answers from pattern.

Scope It Deliberately

Too little context and the agent guesses. Too much and it loses the thread entirely. Point it at the handful of sources that genuinely decide the outcome, keep them current, and resist the urge to connect everything just because you can.

Trust is the thing that decides whether an agent stays in use. Define clearly what it should never handle alone — refunds above a threshold, anything legal, anything involving an unhappy customer — and make escalation the normal path rather than the failure path.