“Agent” has become a slippery word. In practical terms, an AI agent is software that can pursue a goal through multiple steps, use tools and adjust what it does based on intermediate results.
The important part is the loop
A chatbot answers. An agent acts, observes the result and decides what to do next. That loop makes it useful—but also creates more ways for a small mistake to become a large one.
What works today
Agents are strongest when the task has clear inputs, a constrained set of actions and an obvious definition of done. Research briefs, document processing, internal routing and draft preparation are good candidates.
The best deployments usually look less magical than the demos. They contain narrow permissions, structured outputs, validation rules and a human checkpoint before consequential actions.
Where agents still fail
They struggle when success depends on unspoken organisational knowledge, ambiguous judgement or reliable operation across dozens of steps. They may misunderstand an exception, use outdated information or continue confidently after an early mistake.
The first workflow to automate
- Choose a task one person repeats at least weekly.
- Write down its inputs, decisions and outputs.
- Remove steps that do not need to exist.
- Let AI draft or classify, but require approval before sending, deleting or purchasing.
- Measure time saved and corrections required for four weeks.
Bottom line
Do not begin with “Where can we deploy an agent?” Begin with “Which repetitive decision is costing us time?” The technology should disappear behind a better process.