No. Using AI in a workflow does not automatically make that workflow an agent. The key question is who controls what happens next: if application code follows a predefined sequence and an AI model handles one step, it is an AI-powered workflow. If the model can choose actions or tools dynamically, respond to results, and direct progress toward a goal, calling it an AI agent is more defensible.
There is no universal naming standard, and some organizations use “agent” more broadly. Describe the system’s actual behavior—especially its decision-making and human approval points—rather than relying on the label alone.
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What separates an AI workflow from an agent?
The distinction is about control, not the presence of AI, the number of model calls, or how many tools are connected. Anthropic distinguishes workflows, where code orchestrates models and tools along predefined paths, from agents, where a model dynamically directs its process and tool use. Anthropic’s architecture guide also notes that “agent” is used in more than one way.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →OpenAI describes agents as systems that independently accomplish tasks on a user’s behalf. Its guide says the model manages workflow execution and makes decisions, can recognize when a task is complete or correct an action, and selects tools according to the workflow state within guardrails. An application that uses an LLM without letting it control workflow execution is not an agent under this definition. OpenAI’s guide to building agents
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Google for Developers defines an agent as software that can reason about user inputs in order to plan and execute actions on behalf of the user. Its glossary describes an agentic loop of observing, reasoning, acting, and receiving feedback. These are useful signs of agent-like behavior, not a universal naming rule. Google’s machine-learning glossary
Compare the two architectures
| Question | Predefined AI workflow | Model-directed agent |
|---|---|---|
| Who chooses the next step? | Application code follows a designed sequence or routing rule. | The model can choose a next step based on the current state. |
| How are tools used? | Tools are called at specified points in the flow. | The model can select tools dynamically in response to the task. |
| How does it adapt? | Changing what happens usually means editing the workflow or its rules. | It may respond to tool results and revise what it does next. |
| How predictable is execution? | Usually easier to constrain for a clearly defined task. | More flexible, but execution can vary. |
| What can a person do? | Review results or operate the sequence. | Set limits, supervise, approve actions, or resume control. |
| What is the trade-off? | Often suitable when fixed orchestration is sufficient. | Model-driven decisions can add latency and cost in exchange for flexibility on tasks that need it. |
This is an architectural comparison, not a formal certification checklist. Sources: Anthropic and OpenAI.
Use this test before calling something an agent
- Identify who decides the next step. If a fixed chain, router, or script determines the sequence and the model fills in a step, call it an AI-powered workflow or LLM workflow.
- Check whether the model directs execution. If it chooses tools or actions dynamically, reacts to results, and manages progress toward a goal, “AI agent” is a reasonable label under the narrower architectural definitions.
- Describe the human approval boundary. If the model proposes actions but a person must approve them, say so. Supervision does not by itself eliminate all autonomy.
- Separate nested layers. If an agent operates inside a larger fixed process, describe it as an agent within a workflow and explain which layer controls the next step.
This test is a practical way to describe system behavior, not a rule imposed by a regulator or standards body. Definitions vary. The OECD’s 2026 report compares definitions rather than establishing a binding standard; its summary emphasizes systems that perceive and act on an environment with some autonomy, using tools as needed to pursue goals and adapt to inputs and context. OECD, The agentic AI landscape and its conceptual foundations (2026)
When is an agent worth using?
Start with the simplest architecture that fits the task. Anthropic recommends predefined workflows for well-defined work where predictability and consistency matter. A sequence can include multiple model calls and still be a workflow: prompt chaining, routing, and parallelization do not make it an agent when the overall structure is predefined.
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Consider a model-directed agent when a task needs flexibility or decisions that cannot be handled well by conventional deterministic or rule-based approaches. Its ability to adapt may be useful, but the added decision-making can increase latency and cost. OpenAI also emphasizes equipping agents with tools, instructions, and guardrails when they act on a user’s behalf. Anthropic · OpenAI
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why definitions still vary
“Agent” is not a universally settled category. The OECD’s 2026 review finds objectives, outputs—often actions—and autonomy among the most common elements across the definitions it compares. In its selected sample of 18 definitions, all 18 mark objectives and outputs, while 17 mark autonomy. Those counts describe that sample, not every definition in use or an industry-wide survey. OECD report
For readers, teams, and product documentation, a behavioral description is more informative than a bare label. Say whether the model selects tools, adapts to results, can act without approval, and operates within a fixed larger process. OpenAI’s guide summarizes its own definition this way: “Agents are systems that independently accomplish tasks on your behalf.” OpenAI, A practical guide to building agents
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