AI Agent

What It Is

An AI Agent is software that receives a goal and works toward it with limited human supervision. Unlike a chatbot that answers one question at a time, an agent breaks a goal into steps, decides what to do next, calls other tools or systems, and adjusts based on results. You give it an outcome ("reconcile these invoices" or "draft and schedule this campaign") rather than a single instruction. Most agents are built on a large language model that provides the reasoning, connected to your applications through APIs.

Why it matters

Agents move AI from advice to action, and that changes the leadership conversation. A CFO no longer asks only "can AI summarize this report" but "can an agent pull the numbers, flag the variances and prepare the draft commentary." The value comes from delegating multi-step work, but so does the risk, because an agent that acts can also act wrongly at scale. A marketing agent that adjusts ad spend across channels can optimize overnight or drain a budget by morning. This is why governance, spending limits, approval checkpoints and clear audit trails belong in the design from day one. The question for senior leaders is not whether agents are impressive but which decisions you are willing to let them make unattended.

How it works

An agent runs in a loop: observe the situation, plan a step, act (often by calling a tool or system), then check the outcome and repeat until the goal is met or a limit is reached. Its usefulness depends on the tools you connect and the guardrails you set, not on the model alone. In practice a leader meets an agent as a licensed feature inside a CRM, a finance platform or a service desk, configured to handle defined tasks. You set boundaries: which systems it can touch, what it must escalate to a human, and how much it can spend or send. Start with low-risk, reversible tasks, keep a person in the loop for consequential actions, and measure whether the agent actually reduces cycle time and error rates before widening its mandate.