AI Agents Explainer
An AI agent is a system in which a model can decide how to pursue a goal, select tools, take actions and adapt after seeing results. A chatbot may answer a single question, while an agent can carry out a sequence: search for information, compare options, update a document, run a test and check whether the outcome meets a stated condition.
The model is only one part of the system. An agent harness manages instructions, tool definitions, permissions, memory, observations and the loop that returns each result to the model. Some products called agents are actually fixed workflows, where code determines every route. In a more autonomous agent, the model chooses its next step dynamically within boundaries set by the application.
Agents are useful when a task is open-ended, involves several tools and benefits from reacting to intermediate findings. That flexibility has costs. Every extra step adds latency and expense, and an early mistake can affect later actions. Tools also connect the model to real systems, so a wrong click, unsafe command or misleading web page can have consequences beyond an inaccurate sentence.
Reliable designs keep the goal and stopping conditions clear, expose only necessary tools, validate inputs and outputs, record what happened and require human approval before consequential actions. Developers test complete trajectories rather than grading only the final answer. Many tasks still work better as a simple model call or a predictable workflow; autonomy is valuable when it produces a measured benefit, not when it is added for its own sake.
An AI agent uses a model to direct multistep work and tool use. Key terms include agent, workflow, harness, tool, observation, memory, planning, trajectory, guardrail and human oversight.
Anthropic’s guide to effective agents distinguishes fixed workflows from model-directed agents. Its trustworthy-agents report defines agents through model-directed processes and tool use.
The system gives the model a goal and available tools. The model chooses an action, the harness executes it, and the result returns as a new observation.
The loop continues until a stopping condition is met, the agent asks for approval or a limit is reached. Memory can preserve useful state across steps or sessions.
Agents may misread instructions, choose the wrong tool, trust hostile content, repeat actions, lose track of progress or stop before the goal is complete.
Least-privilege access, sandboxing, action limits, audit logs, evaluations and approval gates reduce risk. High-impact actions should stay under accountable human control.
Explore more "Explainers"
Discover additional explainers across politics, science, business, technology, and other fields. Each explainer breaks down a complex idea into clear, everyday language—helping you better understand how major concepts, systems, and debates shape the world around us.