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AI agents are software systems that can choose and carry out steps toward a goal, often by using tools and responding to what happens. Unlike a single chatbot reply or a fixed automation, an agent can decide what to do next within the instructions and permissions it has been given. The term has no single universal definition, so this guide uses that practical working definition.
What are AI agents?
An AI agent combines an AI model with software that lets it take actions and use feedback to pursue a goal. The OECD’s 2026 synthesis describes agents broadly as systems that perceive and act on their environment with some autonomy, using tools as needed and adapting to changing inputs and contexts. In practice, autonomy is a spectrum: an agent might act for several steps, pause for approval, or ask a person to clarify what they mean.
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Anthropic’s practical distinction is that an agent can direct its own process and tool use rather than follow a fully fixed script. That does not mean it is continuously running, self-improving, or dependable without supervision.
| Approach | How it works | Typical outcome |
|---|---|---|
| Chatbot response | The model generates a response to a prompt, usually without taking further action. | Text, advice, or another generated result for the user. |
| Fixed automation | Software follows predefined steps and conditions. | A predictable action sequence when its inputs and rules fit. |
| AI agent | A model can select and sequence actions using tools and feedback, within set boundaries. | A multi-step attempt to reach a goal, with possible pauses for review or clarification. |
The key difference is not that an agent uses AI—chatbots can, too—but that it can decide what action to take next and use the result to shape what follows.
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How does an AI agent work?
A simple agent cycle looks like this:
- Interpret the goal. Work out what the user wants and identify constraints.
- Choose a next step. Plan an action, such as searching a connected source or running code.
- Use an allowed tool. Send the action to an available service or environment.
- Observe the result. Use the tool’s response or execution output as feedback.
- Adjust, stop, or ask. Continue with a revised plan, finish when a stopping condition is met, or request human input.
Anthropic describes the distinction this way: “The practical difference between this and a chatbot is that an agent operates in a self-directed loop: it plans, acts, observes the result, adjusts, and repeats until the task is done or it needs to check in for human input.” This is from its research guidance, “Trustworthy agents in practice” (April 9, 2026).
Example: processing an expense
Anthropic uses expense processing to illustrate the loop: an agent could transcribe receipts, extract vendors and amounts, categorize expenses, and submit them through an expense system. If a hotel charge is rejected because the applicable cap is unknown, it could pause and ask whether it should retrieve the expense policy before proceeding. This illustrates a possible workflow; it is not a guarantee that every agent can handle expense submissions correctly.
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What parts make up an AI agent?
These labels help explain a typical architecture, but products do not all divide responsibilities in exactly the same way.
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- Harness or orchestration: supplies instructions, manages the tool-use loop, and applies guardrails.
- Tools: expose operations such as email, calendar, search, code execution, or expense software.
- Environment: defines where the agent runs and what files, websites, systems, or computing resources it can access.
- Application layer: submits tasks, receives progress, handles functions, and may manage the environment’s lifecycle.
Anthropic discusses the model, harness, tools, and environment in its agent guidance. OpenAI’s API architecture documentation describes the harness, environment, and application server as separate roles, while noting that an environment is not required for every tool-using agent.
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When should you use an AI agent?
An agent is most useful when a goal is open-ended, needs several steps, depends on external information, or requires a tool to act on the result. Examples include researching across connected sources, completing a coding task, or processing an expense workflow. These are use-case illustrations, not promises of accuracy or successful completion.
For predictable work with stable rules—or a task that one model call can complete—a simpler approach may cost less and respond faster. Google Cloud’s guidance recommends choosing an architecture based on task complexity, latency and performance, cost, and the amount of human involvement required. Before choosing an agent, consider:
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- Structure: Can you describe the work as stable steps, or must the system adapt as it goes?
- Number of decisions and tool calls: Does the task need repeated actions and feedback, or is one call enough?
- Cost and latency: Will repeated model and tool cycles be worthwhile for this result?
- Reliability needs: How will you check the output, and what happens if it is wrong?
- Access: Which data and actions does the agent actually need?
- Approval: Which steps require a person to review or confirm them?
Google Cloud’s design-pattern guidance explains the trade-offs between agentic and simpler architectures.
What are the risks?
An agent’s ability to act can save manual steps, but it also creates opportunities for mistakes and misuse. Anthropic notes that less oversight leaves more room for an agent to misread intent or take an unintended action. Prompt injection is one example: malicious instructions can be hidden in content the agent is asked to process. More access to tools and environments can mean more ways for a problem to arise and more consequential outcomes. No single safeguard guarantees protection.
Google Cloud distinguishes between two broad oversight modes. In a human-in-the-middle workflow, a person approves proposed actions; this can reduce risk, but the reviewer must still recognize a harmful or deceptive action. In agent-only operation, safeguards must carry more of the burden, and risks include prompt injection, unsafe chains of tool calls, and weak error handling. See Google Cloud’s security and safety guidance.
Ways to bound an agent’s actions
- Apply least privilege: grant only the access and actions necessary for the task.
- Require review for sensitive actions: keep consequential steps—such as sending, submitting, or changing important records—behind explicit approval.
- Constrain the environment: limit which systems, files, and services the agent can reach.
- Set stopping points: define when it must stop, ask for clarification, or hand off to a person.
- Inspect results and logs: make it possible to review what the agent attempted and what the tools returned.
Match human involvement to the stakes and uncertainty. Anthropic’s framework for developing safe and trustworthy agents emphasizes retaining human control over how goals are pursued, especially before high-stakes decisions. If the user’s intent or preferences are unclear, the agent should ask; consequential actions should require confirmation.
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