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AI agents are software systems that can choose and carry out some of the steps needed to reach a goal. They may use tools, check what happened, and adjust their next action instead of returning a single answer or following a fully fixed script. There is no universal definition, and “agent” does not necessarily mean fully autonomous: a person can approve actions or step in when the system is uncertain.
What are AI agents?
A useful working definition is an AI system that directs some of its own reasoning and tool use while pursuing a task. It can interpret a goal, decide what to do next, act through an available tool, and use the result to choose another step. The OECD’s 2026 synthesis describes agents broadly as systems that perceive and act on their environment with some autonomy, using tools as needed to pursue goals and adapt to changing inputs and contexts. Definitions vary across sources and products, so this is a practical description rather than a formal standard.
The key distinction is not whether software uses AI, but how its steps are selected:
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|---|---|---|
| Single chatbot response | Generates a response to a prompt; the interaction typically ends with that content. | An answer, draft, summary, or suggestion for a person to use. |
| Fixed automation | Runs predefined steps and conditions, generally without choosing a new plan based on open-ended judgment. | A predictable workflow when inputs and rules are stable. |
| AI agent | Selects and sequences actions using model judgment, available tools, and feedback, within its instructions and access. | Progress on a multi-step goal, sometimes with a request for human input or approval. |
These approaches can overlap. An agent may be embedded in a fixed workflow, and an ordinary assistant may call a tool without having the broader ability to plan and repeat actions. Autonomy is a spectrum: an agent can be allowed to propose actions, act only after approval, or proceed within narrow limits. The term alone does not imply continuous operation, self-improvement, or reliable performance.
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How does an AI agent work?
A simple agent follows a feedback loop. It does not need to produce a complete plan in advance; it can choose a next step, observe the result, and revise its approach.
- Interpret the goal. Determine what the user wants and identify missing details or constraints.
- Choose a next step. Decide whether to answer, gather information, use a tool, or ask a person for clarification.
- Take an allowed action. Call an available tool, such as a search service, calendar, code executor, or business application.
- Observe the result. Use the tool or environment response as evidence about what happened, rather than assuming the action succeeded.
- Adjust, stop, or check in. Continue if useful, stop when a defined condition is met, or ask for human input when the goal is unclear or an action needs approval.
Anthropic’s research guidance, “Trustworthy agents in practice” (April 9, 2026), puts 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.” The loop depends on tool access and system design; it is not a guarantee that the agent will choose the right action or recognize every error.
Example: processing an expense
Anthropic illustrates the loop with expense processing: an agent could transcribe receipts, extract vendor names and amounts, categorize expenses, and submit them through an expense system. If a hotel charge is rejected because the applicable cap is unknown, the agent could pause and ask whether to retrieve the expense policy before continuing. This is an illustrative example of a possible workflow, not a claim that every agent or expense system can perform it.
What are the main parts of an AI agent?
Agent systems are assembled differently, but these labels help explain the responsibilities involved:
- Model: Supplies language and reasoning capability used to interpret requests and choose actions.
- Harness or orchestration: Provides instructions, manages the tool-use loop, and applies guardrails such as step limits or approval checks.
- Tools: Expose operations the agent can use, such as email, search, code execution, calendar functions, or expense software.
- Environment: Defines where the agent runs and which files, websites, systems, or computing resources it can access.
- Application layer: Submits tasks, receives progress, handles function calls, and may manage the environment’s lifecycle.
These are architectural roles, not mandatory product boundaries. Anthropic discusses a model, harness, tools, and environment; OpenAI’s API architecture separately describes harness, environment, and application-server roles, and notes that an environment is not required for every tool-using agent. A system can therefore use tools without needing a separate, persistent environment.
When should you use an AI agent?
An agent is most worth considering when a goal is open-ended, takes multiple steps, depends on external information, or requires actions across connected tools. Research across sources, some coding tasks, and expense processing can fit that shape, but whether an agent is appropriate depends on the task and system—not the example alone.
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For predictable work with stable inputs and rules, fixed automation may be easier to test and operate. If a task can be completed with one model call, adding an agent loop may add cost and delay without adding useful capability. Google Cloud’s guidance recommends weighing task complexity, latency and performance, cost, and the amount of human involvement before choosing an architecture.
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- Structure: Can the work be expressed as stable steps, or must the system adapt as it learns new information?
- Tool use: Does the task genuinely require multiple tool calls or decisions, or would one response suffice?
- Cost and latency: Is the benefit of repeated model and tool cycles worth the additional time and operating cost?
- Reliability: How consequential is an incorrect result, and how will someone or something verify it?
- Access: Which data and actions must the system reach to complete the task?
- Approval: Which actions can happen automatically, and which require a person’s confirmation?
If the workflow is high-stakes, hard to verify, or does not benefit from adapting its steps, a narrower automation or a human-led process may be the better fit.
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What are the risks?
More autonomy can make a system useful, but it also gives it more opportunity to misunderstand the goal or take an unintended action. Risk depends on the agent’s permissions, tools, environment, and the consequences of errors. An agent that can read a document presents a different exposure from one that can also send messages, change records, or make purchases.
Misread intent and unsafe actions
A model may misinterpret an ambiguous request, overlook a constraint, or act on an assumption the user did not intend. Anthropic’s guidance emphasizes keeping people in control of how goals are pursued, particularly before high-stakes decisions. Ask for clarification when intent or preferences are uncertain, and require confirmation before consequential actions.
Prompt injection and tool chaining
Prompt injection occurs when malicious instructions are hidden in content an agent is asked to process. Because an agent may read external material and then use tools, hostile content can try to influence later actions. Unsafe sequences of tool calls and weak error handling can create additional exposure. No single defense guarantees protection, and adding tools or reachable systems can increase the number of potential entry points and consequences.
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In a human-in-the-middle setup, a person reviews or approves proposed actions. This can reduce risk, but it depends on the reviewer noticing a harmful or destructive action. In agent-only operation, safeguards must prevent or contain unsafe behavior without that checkpoint; prompt injection, unsafe tool chaining, and poor handling of errors remain concerns. The right level of human involvement depends on the stakes and uncertainty of the task.
Practical ways to bound an agent
- Apply least privilege: Give the agent only the data access and action permissions required for its task.
- Gate consequential actions: Require explicit review before actions such as sending, deleting, submitting, or changing important records.
- Constrain the environment: Limit which files, services, websites, and systems the agent can reach.
- Set clear limits: Define what counts as completion, when the agent must stop, and when it must ask a person.
- Inspect activity: Review logs, tool results, and outputs so failures or unexpected actions can be identified.
These controls reduce exposure but do not eliminate it. Oversight should match both the uncertainty of the task and the impact an incorrect action could have.
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