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An AI agent is a software system that works toward an objective by choosing or directing actions, often using tools, and using the results to decide what to do next. Its defining feature is not simply that it uses AI: it has some delegated control over the steps it takes. That autonomy can be limited by permissions, fixed stages, and human approvals.
What makes a system an AI agent?
A useful practical test is to ask who chooses the next step. In a fixed workflow, the steps are set in advance. An agent is given an objective and capabilities, then can select at least some steps or tools in response to the current context and results.
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OpenAI describes agents as systems that “independently accomplish tasks on your behalf” in its practical guide to building agents. Anthropic similarly defines an agent as a model that directs its own processes and tool use to accomplish a task, rather than following a fixed script, in Trustworthy agents in practice. These are useful working definitions, not a universal standard with a single autonomy threshold.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThe OECD’s 2026 analysis of 18 mapped definitions found that all 18 mention objectives and outputs, 17 mention autonomy, 13 mention influence on an environment, 12 mention adaptiveness, 10 mention inference, and 4 mention data or input. These are counts within that report’s definition mapping, not a census of all AI systems. The report identifies objectives, outputs and autonomy as the most prevalent elements, while showing that definitions differ on other features. See the OECD analysis of AI agent definitions.
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How an agent differs from a chatbot or automation
| System pattern | Who chooses the next step? | Useful description |
|---|---|---|
| Single-turn chatbot or model call | The user gives each instruction, or the system returns one response. | An AI assistant or application; OpenAI’s practical guide does not treat a model that only produces a response as an agent. |
| Deterministic automation | A predefined program or workflow. | Fixed workflow or automation. |
| LLM-based agent | The system selects or directs some steps and tool calls toward an objective, then uses the results to continue or adjust. | An agent; explain what choices it can make and where people intervene. |
| Hybrid system | Some stages are fixed; selected decisions or tool calls are made dynamically. | A hybrid agent and workflow; identify which decisions are delegated. |
The categories can overlap. A system may keep predictable stages in a fixed workflow and delegate context-sensitive decisions to an agent. Calling a system an agent alone does not tell you how capable it is, how often it succeeds, or how much control it has.
What components can an AI agent have?
There is no mandatory component checklist shared by every definition. A practical description can cover the following parts; particular systems may combine them, omit them, or implement them differently.
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- Model: interprets context and helps select decisions or actions.
- Instructions and guardrails: state the objective, limits, approval rules, and conditions for returning control to a person.
- Tools: let the agent retrieve information or act through functions, APIs, or interfaces.
- Environment: the systems and data the agent can access. Its access determines both its capabilities and the potential consequences of its actions.
- State or memory: retains task context or information across steps or interactions. Persistent memory is an architectural choice, not a requirement for every agent.
- Orchestration: coordinates components or agents, and may combine fixed coordination with choices made while the system runs.
OpenAI’s guide emphasizes a model, tools, and instructions; Anthropic describes a model, harness, tools, and environment; AWS also discusses memory and orchestration as useful system concepts. See AWS’s overview of agents in Amazon Bedrock.
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When does an agent make sense?
Agents may be worth considering when a task involves context-sensitive decisions, exceptions, unstructured information, or rules that are difficult to maintain. Those conditions are reasons to evaluate an agent, not proof that one will outperform conventional software. OpenAI recommends examining the workflow and validating that agentic execution is justified; stable, clearly specified steps may be better handled by deterministic software.
When evaluating a design, consider these dimensions:
- How much of the control flow the model can choose.
- Which tools it can use, and what each tool is permitted to change.
- What data and systems are accessible.
- Whether task state or memory persists between steps or interactions.
- Where approval, escalation, and handoff to a person occur.
- How accuracy, cost, and latency will be evaluated. OpenAI recommends establishing an evaluation baseline before optimizing cost and latency.
Why autonomy needs boundaries
A system with more discretion has more opportunity to misunderstand a request or take an unintended action. Tool access can make those errors consequential: retrieving a record is different from changing one. Anthropic also identifies prompt injection as an attack that may try to induce costly actions.
For a real system, describe the boundaries alongside its capabilities: which tools are available, what they can change, which actions require confirmation, and when the agent must stop or hand work to a person. Human approval and restricted permissions are design controls, not evidence that a system is—or is not—an agent.
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