01The loop: plan, act, check
An agent receives a goal, decides the next step, executes it with a tool, looks at the result and repeats until the goal is met. The language model is the reasoning engine; tools are its hands.
Good agents are constrained: they have a short list of tools, explicit instructions and examples of good output. Constraints make them reliable.
02Where data comes from
Agents read from the systems you connect—order data, tickets, product information, campaign briefs. Quality of output is mostly quality of input and instructions, not the model brand.
03Where results go
In a well-designed setup, results come back to the person who asked, ready to approve. Running agents inside Teams and Slack keeps that loop in the conversation your team already uses.