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Use a workflow when the steps are known and repeatable; use an AI agent when the system needs to decide what to do next as conditions change. Between those choices, a workflow with one bounded LLM-powered step is often enough. The right architecture depends on whether adaptation improves the result enough to justify added latency, cost, and engineering complexity.
What distinguishes an AI agent from a workflow?
The key distinction is who controls the path through a task. In a workflow, code determines the sequence of steps and branches. In an agent, the model can choose tools and next actions, and revise its approach as it receives information, within the permissions and limits you set.
Terminology is not universal: different organizations use “agent” differently. Here, “workflow” means predefined control flow, and “agent” means model-directed execution. Anthropic makes this same control-flow distinction in its engineering guidance; OpenAI describes the related patterns in its practical guide to building agents.
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Conventional workflow
Code orchestrates the steps, tools, and branches in advance. This suits tasks whose requirements can be expressed reliably as a sequence of actions and rules.
Workflow with a bounded LLM step
The overall sequence remains fixed, but a model interprets one part of the input—for example, classifying a request, summarizing a document, or extracting fields. The workflow then resumes control. The model is not responsible for planning a chain of actions.
Agent
The model receives a goal and instructions, then chooses tools or subsequent steps based on the information available. It may adapt its plan during execution, so its tool access, guardrails, and stopping conditions matter.
When should you choose each architecture?
Start by asking whether you can specify the task path before a run begins. If you can, a workflow is usually the simpler starting point. If the system must decide which information to gather or which action to take next based on changing context, an agent may be worth evaluating.
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| Decision factor | Workflow or bounded LLM step | Agent |
|---|---|---|
| Task path | Steps and branches can be specified in advance. | The needed subtasks or their order may vary between runs. |
| Judgment | Rules cover the cases, or interpretation is confined to one step. | Context, exceptions, or unstructured information affect what happens next. |
| Changing conditions | A defined error route or escalation to a person is adequate. | The system needs to gather alternate evidence, select another tool, or revise its plan. |
| Predictability | Repeatable, predetermined execution is a priority. | Flexibility is valuable enough to accept a less predetermined path, with appropriate oversight. |
| Operational trade-off | Additional model loops would add burden without enough benefit. | Evaluation shows that adaptive execution materially improves the outcome. |
OpenAI identifies complex decision-making, rules that are difficult to maintain, and heavy reliance on unstructured data as signals to consider an agent. If those conditions are not clearly present, a deterministic solution may be sufficient. Anthropic also notes that some applications need only a well-designed single LLM call, potentially paired with retrieval and examples. Neither set of signals makes an agent the default: test whether the flexibility helps your specific task.
Why a hybrid is often the practical choice
You do not have to choose one architecture for an entire application. Keep known sequencing, validation, and handoffs in code; use an LLM for a bounded judgment where rules alone are insufficient. Introduce an agent loop only for the portion where the next action genuinely depends on what the system learns.
OpenAI’s business guide to working with agents illustrates the distinction with account security after repeated failed logins: a fixed rule can trigger a predefined response; an LLM-powered workflow can interpret recent location and risk data within a known process; an agent can analyze data, use tools, update its plan, and decide what to do. This is an architectural illustration, not evidence that one approach is universally safer or more accurate.
How to account for cost, latency, and oversight
Agentic execution can trade additional latency and cost for better performance on tasks that benefit from adaptation. Engineering and operational complexity can also rise as the model gains choices and tools. There is no universal break-even threshold established by the cited guidance, so compare architectures on your own workload rather than relying on generic cost or speed claims.
- Measure the task outcome: Compare whether adaptive execution improves the result enough to justify the added steps.
- Track operational burden: Account for evaluation, maintenance, observability, and the complexity of handling failures.
- Bound actions: Give an agent only the tool permissions needed for its task, add guardrails, and define when it must stop or seek human intervention. OpenAI discusses these controls in its guide to building agents.
- Keep a suitable review path: As the system can take more actions, make approval points and human escalation part of the design where the consequences warrant them.
When are multiple agents justified?
Begin with one agent, adding tools and clarifying instructions incrementally. A single agent is often simpler to evaluate and maintain. Consider splitting work across agents when conditional logic is becoming hard to manage, tool selection remains unreliable despite clearer tool definitions, or separating roles materially improves performance, scalability, policy isolation, or trace legibility. Multiple agents add orchestration overhead; they are not automatically an upgrade.
Handoffs versus agents as tools
Once you decide to use multiple agents, choose how responsibility moves. In a handoff, control passes to a specialist that owns the next user-facing response. With agents as tools, a manager calls bounded specialists and remains responsible for synthesizing the answer. These are orchestration choices, not alternatives to the workflow-versus-agent decision. OpenAI describes both in its orchestration and handoffs guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical decision sequence
- Map the task. Write down its steps, branches, tools, and failure routes. If these are stable, implement them as a workflow first.
- Find the ambiguous step. If only one bounded part needs interpretation, keep the workflow in control and use an LLM there.
- Identify where the path must adapt. Consider an agent for the portion where new information changes which tool or action is appropriate.
- Define limits and review. Specify permitted tools, guardrails, approval points, escalation, and a stopping condition before expanding autonomy.
- Evaluate before adding complexity. Compare outcome quality and operational costs on representative tasks; add agent loops or specialist agents only when the gains justify them.
The underlying architecture guidance is more durable than any particular framework or API label. Anthropic’s article was published on December 19, 2024 and notes that tooling changes over time; consult current official documentation when implementing a specific framework or interface.
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