An AI agent is software that combines a large language model with the ability to call tools (search the web, query a database, send an email, run code) and decides on its own which steps to take to complete a task. Agents are the practical step beyond chatbots: instead of just answering, they act. A useful agent has clear goals, narrow tool access, and a way to verify results before they ship.
How an AI agent works
The core loop is straightforward. The agent receives a goal, picks a tool from a defined list, runs it, observes the result, and decides whether the goal is reached or another step is needed. The model itself does not store data between steps; instead, the orchestrator passes context back into the prompt at each iteration. Common tools include web search, code execution, file reads, calendar APIs, CRM updates, and email drafting.
Where agents are useful for businesses
Agents earn their keep on tasks that require multiple steps, reading or writing across multiple systems, or judgment that a fixed script cannot encode. Lead-intake assistants, meeting follow-up systems, knowledge-base searchers, and quote drafting are common starting points. The pattern that fails: throwing an agent at a problem that already had a clean rule-based solution. Pick agents for open-ended work, not deterministic work.
Where agents fail
Agents fail when they have access to too many tools, when their tools have ambiguous return values, or when no one verifies the output before downstream effects occur. The most common production failure mode is silent: an agent succeeds 95% of the time and the 5% failure case quietly emails the wrong client. Mitigation: narrow tool access, require human-in-the-loop on irreversible actions, log every tool call.