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 autonomy is bounded: a person or the system’s rules may limit its permissions, require approval, or take over at defined points.
What makes software an AI agent?
The key distinction is not simply whether a system uses AI. It is how the work proceeds. A fixed workflow follows steps selected in advance; an agent has some delegated choice over which step or tool to use next, based on the current task and the results so far.
OpenAI’s practical guide describes agents as systems that “independently accomplish tasks on your behalf.” Anthropic defines an agent as a model that directs its own processes and tool use while accomplishing a task, rather than following a fixed script. These are practical definitions, not a universal standard: the boundary depends on how much control the system actually has.
In everyday terms, an AI agent generally has three characteristics:
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- A goal: It is set up to complete a task, not just return an isolated response.
- Actions: It can do more than generate text; it may search, call an API, update a record, or interact with another system.
- Delegated decisions: It chooses or directs at least part of the process, then can use the outcome to continue or adjust its work.
There is no single autonomy threshold that every source or standard uses. An agent may operate only within a narrow task, fixed stages, limited permissions, and human approval checkpoints.
How an AI agent works
A practical way to understand an agent is to look at the components that let it interpret a goal, act, and respond to results. Not every system needs every component, and implementations use different architectures.
Model
The model interprets the available context and helps select a response or action. Its role may include deciding which tool to call or what information is still needed.
Instructions and guardrails
Instructions define the task and its limits. Guardrails can restrict actions, require approval, or tell the system when to stop and hand control to a person.
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Tools
Tools connect the agent to information or actions outside the model. They may retrieve data, call APIs, or make changes in an external system. A tool’s permissions determine what the agent can actually do.
Environment
The environment is the data and systems an agent can access. An agent that can read a document has different capabilities—and different risks—than one that can change a customer record or submit a payment.
State or memory
Some systems retain task context or information across steps or interactions. Persistent memory is an architectural choice, not a requirement for something to count as an agent.
Orchestration
Orchestration coordinates steps, components, or multiple agents. It can combine predetermined stages with decisions made dynamically while a task is running.
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AI agent vs. chatbot vs. automation
| System pattern | Who determines the next step? | Useful description |
|---|---|---|
| Single-turn chatbot or model call | The user supplies the next instruction, or the system returns one response. | An AI assistant or application. Under OpenAI’s practical guide, a system that does not control workflow execution is not an agent. |
| Deterministic automation | A predefined program or workflow. | Fixed automation: the steps are specified in advance. |
| AI agent | The system selects or directs some steps or tool calls toward a goal and uses results to continue or adjust. | An agent; describe its actual autonomy and human checkpoints. |
| Hybrid system | Some stages are fixed; selected decisions or tool calls are dynamic. | A hybrid agent and workflow. Specify which decisions are delegated. |
These categories are not always mutually exclusive. A system can use fixed stages for predictable work and delegate particular choices to an agent. The useful question is not only whether a product is marketed as an agent, but which decisions it makes and what it is allowed to change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When does an agent make sense?
Agents may be worth considering when work involves context-sensitive decisions, exceptions, unstructured information, or rules that are difficult to maintain. Those traits are reasons to evaluate an agent, not proof that an agent will outperform conventional software.
If the steps are stable and easy to specify, deterministic software may be simpler and more predictable. Before choosing an agent, map the task and ask:
- Which decisions are genuinely difficult to encode as fixed rules?
- What information and systems must the software access?
- What actions can it take, and which should require confirmation?
- Where should it stop and hand the work to a person?
- How will accuracy, cost, and latency be evaluated against a baseline?
Useful design comparisons include how much control flow the model owns, the scope of its tools and permissions, the data and environment it can access, whether it keeps state, and where approval or handoff occurs. Establish an evaluation baseline before optimizing cost or speed.
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Risks and oversight
An agent with more freedom has more opportunity to misunderstand a request or take an unintended action. Tool access also creates security concerns: prompt injection, for example, can attempt to manipulate an agent into actions its user did not intend.
Make autonomy concrete rather than treating it as a product label. For each deployment, establish:
- Available tools: What can the agent read, retrieve, create, or change?
- Permission limits: Which actions are prohibited or restricted?
- Approval points: Which consequential actions require a person’s confirmation?
- Stop and handoff rules: When should the agent pause, report uncertainty, or return control?
The more consequential the available actions, the more important it is to design permissions and human checkpoints around them.
How common is this definition?
The OECD’s 2026 analysis mapped 18 definitions of AI agents. Objectives and outputs appeared in all 18, autonomy in 17, influence on the environment in 13, adaptiveness in 12, inference in 10, and data or input in four. These are counts from the definitions reviewed in that report—not measurements of all AI systems or every definition in circulation. The pattern supports a practical definition centered on goals, actions, and some degree of autonomy, while showing why no single boundary is universally accepted.
Quick Recap
Sources
- OECD, AI agents
- OpenAI, A practical guide to building agents
- Anthropic, Trustworthy agents in practice
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