AI agents are one of the year's most used – and most misunderstood – terms. An agent isn't just a chatbot with a new label. The difference lies in what the system is allowed to do on its own, and that changes which tasks AI can take on.
The difference from a regular chatbot
A chatbot answers what you ask, one question at a time. An agent is given a goal and works its way there in several steps: it plans, gathers information and uses tools – searching the web, calling an API, reading a database – and adjusts course based on what it finds.
Put simply: the chatbot tells you how to do something. The agent does it.
Where agents help most
Agents suit tasks that are multi-step but well defined: research across many sources, compiling material, enriching and cleaning data, and monitoring and sorting incoming cases. Work that is time-consuming for a person but follows a clear logic.
In go-to-market the value often sits right here. An agent can research an entire account, weigh signals against each other and hand over finished material – while the rep spends their time on the conversation.
Where to be careful
The more an agent does on its own, the more important the controls become. For tasks where accuracy is critical, or where an action is hard to undo, we build in gates: human review at the right points and clear limits on what the agent may do.
Our rule of thumb is to start narrow – one task, well defined – and expand the agent's responsibility only once it has proven reliable.