AI Agent
Also: AI agent, Artificial Intelligence Agent, AI-Agent, Agent IA, autonomous agent, KI-Agent, agent autonome
Software that pursues a goal on its own: it plans steps, uses tools and takes actions with limited human input.
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.
Frequently asked questions
What is an AI agent?
An AI agent is software that receives a goal and works toward it with limited human supervision. It breaks the goal into steps, decides what to do next, calls other tools or systems through APIs, and adjusts based on results. Most agents are built on a large language model that supplies the reasoning layer.
What is the difference between an AI agent and a chatbot?
A chatbot answers one question at a time; an AI agent pursues an outcome over several steps. You give a chatbot an instruction and a agent a goal, like "reconcile these invoices" or "draft and schedule this campaign". The agent then plans, acts through connected systems and checks its own results until the goal is met or a limit is reached.
How does an AI agent actually work, step by step?
An AI agent runs in a loop: it observes the situation, plans a step, acts (usually by calling a tool or system), then checks the outcome and repeats. The loop stops when the goal is reached or a preset limit is hit. Its usefulness depends on the tools you connect and the guardrails you set, not on the model alone.
What are the main risks of letting an AI agent act on its own?
An agent that acts can also act wrongly at scale. A marketing agent adjusting ad spend across channels can optimize overnight or drain the budget by morning, which is why spending limits, approval checkpoints and audit trails belong in the design from day one. The real decision for a leader is which actions an agent may take unattended.
Where should I start if I want to deploy an AI agent in my company?
Start with low-risk, reversible tasks and keep a person in the loop for consequential actions. Define boundaries up front: which systems the agent can touch, what it must escalate to a human, how much it can spend or send. Measure whether it genuinely reduces cycle time and error rates before widening its mandate. In most companies the first encounter with an agent is a licensed feature inside a CRM, a finance platform or a service desk.