What is an AI agent? A plain-English guide for business

Type "reconcile yesterday’s Stripe payouts against Xero and flag anything that doesn’t match" into a chat box, and a chatbot writes you instructions for doing it yourself. An AI agent does something different: it reads the payouts from Stripe, pulls the matching entries from Xero, compares them line by line, and hands back a sheet with two mismatches highlighted and the fee difference totalled. Same sentence in. One gives you words about the task; the other gives you the task done. That gap, between describing work and completing it, is what the word "agent" actually means.

An AI agent is software that takes a goal in plain language, decides the steps to reach it, and acts across real tools to get it done, checking its own progress as it goes. The key word is acts. A chatbot talks. An agent does.

The perceive, plan, act loop

Strip away the jargon and every AI agent runs the same three-part loop. It is the same loop a competent coworker runs without thinking about it.

1. Perceive: take in the goal and gather the facts it needs, reading an email, pulling a report, checking a record.
2. Plan: work out the steps. Which tools, in which order, to turn the current state into the goal.
3. Act: carry the steps out, using the actual tools, then check the result against the goal and loop again if it is not there yet.

The loop is what separates an agent from a one-shot answer. A chatbot perceives and responds once. An agent keeps going, observing what happened after each step and adjusting, until the goal is met or it needs a human. Ask it to chase five overdue invoices and it does not stop after drafting one email; it works through all five, checks each was handled, and reports back.

What makes software "agentic" versus a chatbot or a script

Three things separate an agent from the tools it gets confused with. Miss any one and you have something simpler wearing the label.

• Versus a chatbot: an agent can act on the outside world, not just generate text. A chatbot answers; an agent sends the email, updates the record, builds the report.
• Versus a script or a Zap: an agent decides the steps itself from a goal. A script runs a fixed path you defined in advance; an agent works out the path at runtime and adapts when the input is not what it expected.
• Versus a single model call: an agent runs the loop, using tools and checking results across many steps, rather than producing one response and stopping.

A useful test: if you have to draw the exact steps in advance, it is a script. If it only produces words, it is a chatbot. If you can hand it a fuzzy goal and it figures out and performs the steps, that is an agent.

What that looks like in a business

The abstraction lands when you see the requests. Each of these is a plain-English goal in, a finished result out, with the agent choosing the route between.

• Finance: "Pull last month’s revenue and write a one-page board brief." → a finished summary, not a tutorial on building one.
• Sales: "Research this inbound lead and tell me if they fit our ICP." → a brief with company size, fit, and a recommendation.
• Support: "Triage the new support inbox and draft replies for the routine ones." → drafted responses, held for review.
• Operations: "Summarise this 80-message thread and list the decisions and owners." → a recap with action items.

None of these end with advice. They end with the work done, because the agent used your real tools to do it.

What to look for when choosing one

Not everything called an AI agent runs a real loop or acts safely. A few things separate a capable, trustworthy one from a demo.

• Approval before acting: this matters most. The agent should read your data freely but pause for your yes before anything irreversible, sending, editing, deleting, paying. An agent that acts unattended on your tools is a liability, not a feature.
• Real tool access: it should connect to the tools you already use, so it acts on live data rather than guessing from what you paste in.
• Finished output: it should return the completed artefact, not a conversation about how to make it.
• Memory: it should remember context between tasks so you are not re-briefing it every time.
• Privacy: confirm it does not train its models on your data, especially once it can read your inbox, CRM, and books.

Of these, the approval gate is the one to insist on. Reading your data and acting on it are different categories of trust, and a well-built agent treats them that way.

Where Echo fits

Echo is an AI agent, an AI employee, that lives in Slack. You describe an outcome in plain English and it runs the loop, using your connected tools to do the work, then returns the finished result in the channel where you asked. It connects to over 3,000 tools, from Stripe and HubSpot to Gmail, Notion, and Xero. It asks for approval before it sends, edits, or moves anything external, remembers your context between tasks, and does not train on your data. It does not charge per seat, so the whole team can delegate without the bill climbing per head. The first $50 of work is free, which is enough to point it at a real task and watch it finish. Start at /signup.