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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →An AI agent is software that uses an AI model to pursue a goal by controlling at least part of a task’s workflow. It decides what to do next, uses tools or connected systems to gather information or take action, and keeps going until it finishes, fails, or hands off to a person. Definitions vary by vendor, but the dividing line is consistent: if the model only produces a single response and does not steer the workflow, it is probably not an agent.
How vendors define an agent
There is no standards-body definition, so the clearest guidance comes from the companies building these systems. Two are worth quoting, with organizational attribution.
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- OpenAI, in its practical guide to building agents: “Applications that integrate LLMs but don’t use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are not agents.”
- Anthropic, in Trustworthy agents in practice (April 9, 2026): an agent is “an AI model that directs its own processes and tool use when accomplishing a task—that is, deciding for itself how to achieve what users want, rather than following a fixed script.”
Google Cloud’s explainer on AI agents (last updated April 2, 2026) covers the same ground with a broader catalogue of types. The shared idea is workflow control, not just text generation.
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Key characteristics
Goal-directed
An agent is given an outcome or task, not just a prompt for one reply.
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Decision-making
The model selects or adapts steps based on the task and what it learns along the way, rather than following a fixed script.
Tool use
Agents retrieve information or act through APIs, functions, or connected applications. Data tools fetch context. Action tools can change records or send messages. What an agent can reach and do matters as much as which model powers it.
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Iterative execution
Results from one step inform the next. The loop ends at a final output, a tool boundary, an error, or another defined exit condition.
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Bounded autonomy
Instructions, guardrails, permissions, and human handoffs limit what the agent may do. Sound designs define boundaries, failure behavior, and when a person must approve or take over.
Optional capabilities
Planning, retained context or memory, multimodal inputs, and multi-agent coordination appear in some designs but are not required. Not every product marketed as an “agent” has all of them, learns persistently, or can safely act unsupervised. When evaluating one, ask what it can access and which actions it is allowed to take.
What is inside a basic agent
A minimal agent has three parts: a model that interprets the task and picks steps, instructions that define its role, goal, and limits, and tools that connect it to data or actions. Implementations may add guardrails and approvals, structured outputs, sessions, context management, and handoffs to other agents (see OpenAI’s agent definitions and Agents API overview).
OpenAI’s current documentation suggests starting with one focused agent and adding more only when different ownership, instructions, tools, or approval policies justify it. That is vendor guidance, not a universal rule.
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These are documented use-case patterns, not evidence of measured performance or adoption.
Best Value
| Example | What it does | Source |
|---|---|---|
| Customer support | Investigates a request using customer and policy information, proposes or carries out an allowed resolution, and escalates when uncertain or approval is needed. OpenAI uses refund approval as its context-sensitive example. | OpenAI guide |
| Data analyst | Answers warehouse questions using read-only SQL. | OpenAI Agents API overview |
| Workplace assistant | Investigates requests using connected workplace tools, such as a Slack bot. | OpenAI Agents API overview |
| Document reviewer | Checks documents against policies and hands issues to specialist agents or people. | OpenAI Agents API overview |
| Scheduled work | A workspace agent starts on a schedule or manual run, follows a process, and interacts with connected systems. | OpenAI Academy, Workspace agents (April 22, 2026) |
Agent vs. chatbot vs. fixed workflow
- Single-turn LLM or simple chatbot: produces a reply; does not control the workflow. Not an agent under OpenAI’s definition.
- Fixed automation: follows predetermined rules. Predictable and easy to test, but brittle with ambiguity.
- Agent: chooses steps and tools at run time within set limits.
When an agent is the right choice
OpenAI’s guide recommends looking for tasks with complex decisions, rules that are hard to maintain, or heavy use of unstructured data, and validating the use case before building. Where rules and outcomes are clear, a deterministic workflow is often easier to manage. These questions, which synthesize that guidance rather than form a formal standard, help decide:
- Task ambiguity: are inputs and exceptions predictable, or must the system interpret context?
- Action risk: does it only draft or retrieve, or can it commit changes, send messages, or trigger transactions?
- Tool access: which records, APIs, and applications can it reach, and what is it permitted to do?
- Oversight and recovery: what needs approval, and how does it stop or hand off when blocked or unsure?
- Reliability: can the full workflow be tested on representative cases and monitored for failures?
- Cost and burden: does adaptive decision-making justify the extra runtime and maintenance over fixed automation?
Specific platform features change often, so check current documentation before relying on any capability described here.
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