AI Agents for Business: A Practical Introduction

Three ingredients separate an agent from a chatbot: a goal it works toward across many steps, tools it can call to read or change the world, and a loop that lets it observe the result and decide the next action. A support chatbot answers; a support agent looks up the order, checks the policy, drafts the response, and escalates when the refund exceeds a limit.

This distinction matters because it changes the risk profile. A chatbot that is wrong costs you a bad answer; an agent that is wrong can take a bad action. Everything about agent design follows from that difference.

The pattern to look for: a workflow done many times per week, with steps that follow a policy a person could write down, where the inputs arrive digitally (email, forms, tickets, spreadsheets), and where errors are catchable by a reviewer. Document intake and routing, CRM hygiene, lead research, invoice matching, and status-chasing across systems all fit.

The pattern to avoid: workflows where the cost of a wrong action exceeds the savings, where the policy exists only in one expert's head, or where the input data is not accessible to software. An agent cannot fix a process you cannot describe.

Every agent workflow has a dial between "agent acts alone" and "agent prepares, human approves". Start every workflow at the human-approval end and move the dial only where the evidence justifies it. Drafting emails for review is a safe start; sending them autonomously is a decision you make later, per workflow, per action type.

Design the escalation path as carefully as the happy path. What happens when confidence is low, a tool fails, or the request is out of policy defines the production quality of the system far more than the model choice does.

Pick one workflow, run the agent in "recommendation mode" alongside the human process for two to four weeks, and measure agreement. When the agent's recommendation matches what the human did often enough, promote that action type to semi-autonomous with spot checks.

This staged path costs little, produces real accuracy numbers on your data, and builds the internal trust that a big-bang autonomous launch never survives.