What are AI agents?

At a glance

• An agent does work, not just talk: it books the meeting, issues the refund, updates the CRM — across steps and systems.
• Its defining trait is autonomy. A copilot waits for your approval; an agent decides its own next move^[4].
• More autonomy means more leverage and more risk — an agent that can act can also act wrongly, at machine speed^[3].
• Beware “agent washing”: many vendors rebrand a chatbot or rules engine as an agent.

How it differs

A fixed automation follows the exact rules you wrote in advance. A chatbot can explain a refund but can’t issue one — it produces words, not actions. An agent reads the message, checks the order, decides if it qualifies, issues it, and updates records — choosing each step itself^[1].

Why it matters

Agents pay off on multi-step tasks that once needed a person stitching systems together: routing tickets, reconciling invoices, qualifying leads. The result is fewer handoffs and more consistent follow-through^[5]. As of early 2026 they have moved into production, with the clearest returns in customer service and operations^[2].

How to adopt without getting burned

Start narrow: one high-volume workflow with a clear success metric. Scope the agent’s access to exactly what that job needs. Keep a human approving irreversible actions — sending money, deleting data — until it has a track record.

Bottom line

An AI agent is the autonomy dial turned up: far more useful than a chatbot, and far more capable of damage if pointed at the wrong job — so start narrow and widen only as it proves itself.