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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss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 →An AI step makes a model call inside a process whose next steps are already defined. An AI agent can choose among permitted actions, use tools, inspect what happens, and continue or stop to reach a goal. The useful distinction is not whether a workflow uses a language model; it is how much decision-making the model controls while the workflow runs.
What is the difference between an AI agent and an AI step?
A basic AI step takes an input or prompt and returns a model-generated output. The surrounding workflow decides what happens next. For example, a fixed process might send a support message to a model for classification, then route it according to a predefined rule.
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An agentic system gives the model more runtime choice. It can interpret a goal, select an action from an allowed set, call a tool such as an API or database, inspect the result, and decide whether to act again or return an answer. The system designer establishes the available tools and boundaries; the model chooses among permitted next actions using the current context.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches| Question | AI step in a fixed workflow | Agentic system |
|---|---|---|
| Who determines the next step? | The workflow designer, through predefined sequencing or rules | The model can choose among permitted actions at runtime |
| How does it use tools? | A workflow may call a tool at a predefined point | The model may select a tool, use its result, then continue or stop |
| Typical task shape | Predictable, structured work suited to a single model call or fixed path | Open-ended, multi-step work that may need to adapt to intermediate results |
A model call alone does not make a system an agent. Nor does an agent have to be fully autonomous, permanently learn, or use multiple agents. Agency is a spectrum: a conventional workflow can include a model-driven routing choice, while a more autonomous agent can plan and act through repeated tool calls.
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How does an agent loop work?
- Interpret the goal. The model receives the task, instructions, and relevant context.
- Choose an action. It selects from the tools and actions the system permits, or decides that no tool call is needed.
- Use a tool. The system executes the selected function, API request, database query, or other allowed action.
- Inspect the result. The model uses the returned information to decide what to do next.
- Continue or stop. It may take another permitted action, ask for human input, or return a response when a stopping condition is met.
A fixed workflow can contain one or more AI steps without giving the model control over the sequence. Conversely, an agent loop can sit inside a larger process that is orchestrated conventionally. The important design question is where runtime decisions happen, not whether the overall system is labeled an agent.
What components can an agent use?
Microsoft’s adoption guidance describes five useful design dimensions. They are not a checklist that every simple agent must include:
- Generative model: the reasoning engine that interprets context and selects a response or action.
- Instructions: rules that define the task, scope, and expected behavior.
- Retrieval: access to relevant information that can ground the model’s responses.
- Actions: functions, APIs, or connected systems the agent can use.
- Memory: conversational history or other state retained for the task.
These pieces serve different purposes. Instructions and tool permissions constrain what the agent can do; retrieval supplies task-relevant context; and memory can help preserve state across interactions. Their inclusion should follow the task rather than the desire to make a system seem more sophisticated.
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When should you use an AI step or fixed workflow?
Prefer a single model call or deterministic workflow when the task is predictable, highly structured, and can be completed without adapting to a sequence of uncertain results. This usually keeps the design simpler and may reduce cost and complexity. Google Cloud’s Cloud Architecture Center guidance, “Choose a design pattern for your agentic AI system,” states: “If your workload is predictable or highly structured, or if it can be executed with a single call to an AI model, it can be more cost effective to explore non-agentic solutions for your task.” The page was last reviewed on 2026-05-28.
A fixed process can still use an LLM where it adds value—for example, to classify an item or suggest a route—while rules determine the subsequent actions. Use this approach when the model’s role is narrow and the rest of the sequence can be specified in advance.
When is an agent a better fit?
Consider an agent when the task is goal-focused and open-ended, requires several steps, needs external information or tools, or must adapt based on what those tools return. Its ability to choose and revise actions can help with work that is difficult to represent as one stable sequence.
That adaptability comes with trade-offs. An agent may make different decisions across runs, and each additional decision or tool call creates more behavior to test and govern. Compare the two designs against the actual task:
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- Variability and complexity: Does the work follow a stable path, or do intermediate results change what should happen next?
- Tool needs: Must the system retrieve current or external data, or take actions in other services?
- Latency and operating cost: Can the task tolerate iterative model calls and tool execution?
- Accuracy requirements: How costly is an incorrect interpretation, action, or final answer?
- Human judgment: Which decisions need review or approval before the system proceeds?
Should you build a single agent or multiple agents?
A single-agent design is a sensible starting point for many agent applications. It keeps coordination relatively direct while letting one agent work through the task’s permitted tools and steps.
Multiple agents can make sense when distinct responsibilities justify splitting the work. But coordination adds orchestration and creates further concerns around communication, reliability, security, evaluation, and cost. Decompose the task only when those separate roles offer a concrete benefit over one agent or a fixed workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you keep agentic automation under control?
More autonomy can make a system adaptable, but it also increases nondeterminism and the burden of testing and governance. Design the boundaries before granting an agent access to consequential actions.
- Limit permissions: expose only the tools and operations required for the task.
- Validate inputs and outputs: check data before it reaches tools and verify results before they drive further actions.
- Ground answers when needed: provide reliable retrieved context for tasks that depend on specific information.
- Require human approval at consequential points: pause before high-impact actions or decisions that require subjective judgment.
- Account for untrusted content: prompt injection and plausible but incorrect model outputs are real risks when the system reads outside content or can use consequential tools.
Approval does not have to mean a person reviews every routine step. It can be placed at a particular action or decision boundary, leaving lower-risk work to proceed within defined limits.
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What to remember about agent vs. AI step
- An AI step returns a model output within a sequence the workflow defines.
- An agent can choose among permitted actions, use tools, and adapt to their results.
- Fixed workflows suit stable tasks; agents are worth considering when a task needs flexible, multi-step action.
- Start with the simplest design that meets the task, and constrain agent permissions and approval points deliberately.
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