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Use a workflow when the steps and branches are known; use an agent when the system must decide what to do next as conditions change. If only one step needs interpretation, keep the workflow in control and add a bounded LLM step. That hybrid is often the simplest effective design.
These labels describe control flow, not a universal product standard. Here, a workflow follows a path defined in advance, while an agent uses a goal and instructions to choose actions or tools during execution.
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What distinguishes a workflow from an agent?
In a workflow, code determines the sequence of steps and branches. An LLM may still perform a task within that sequence, but it does not take over deciding the overall path. Anthropic describes workflows as LLMs and tools orchestrated through predefined code paths, in contrast with agents that dynamically direct their process and tool use: Anthropic’s engineering guidance.
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An agent receives a goal and instructions, then uses the information available to select tools or next steps. It may revise its plan as it learns more, within its permitted tools and safeguards. OpenAI likewise distinguishes workflow automations, which follow predefined steps and rules, from agents that can make decisions and use tools: OpenAI’s business guide to working with agents.
Terminology varies among vendors. Comparing the system’s actual control flow is more useful than relying on its label.
When should you choose each architecture?
| Decision question | Workflow or bounded LLM step | Agent |
|---|---|---|
| Can you specify the task path? | Yes. Steps, branches, and acceptable outcomes can be defined reliably in advance. | No. The needed subtasks or their order depend on what the system discovers during execution. |
| Where is judgment needed? | Rules cover the cases, or one bounded step needs interpretation. | Context, exceptions, or unstructured information should influence what happens next. |
| What should happen when a condition changes? | A defined error path, retry, or human escalation is enough. | The system needs to seek different evidence, choose another tool, or revise its plan. |
| How important is predictability? | Repeatable, predetermined execution and straightforward auditing are priorities. | Flexibility is valuable enough to accept less predetermined execution, with appropriate controls. |
| Does autonomy justify its overhead? | Extra decision loops would not add enough value to warrant their cost and complexity. | Evaluation shows 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 needs are not present, a deterministic solution may suffice: OpenAI’s practical guide to building agents.
There is no universal cost or latency threshold at which an agent becomes worthwhile. Measure the full task on your workload, including failures and human review, rather than assuming greater autonomy automatically means better performance.
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When is a bounded LLM step enough?
A workflow can remain in charge while an LLM handles a contained task such as classification, summarization, or extracting fields from unstructured text. The workflow then validates the result and continues along its planned route. The model interprets information; it does not independently plan a chain of actions.
This distinction is useful when most of a process is stable but one step resists simple rules. For example, a support workflow might use a model to classify a message, then apply fixed routing rules based on the classification. If the system instead needs to investigate the message, select among tools, and choose what to do based on what it finds, an agent may be appropriate for that portion.
Why a hybrid is often the practical choice
You do not have to replace an entire workflow with an agent. Keep known sequencing, validations, and handoffs deterministic; use an LLM where interpretation is needed; add agentic control only where the next action genuinely cannot be specified in advance. OpenAI’s business guide describes combining workflow automation, LLM-powered steps, and agents: OpenAI’s guide.
Consider account security after repeated failed logins. A fixed workflow might apply a predetermined rule. A workflow with an LLM might interpret recent location and risk information before following defined next steps. An agent might gather information using permitted tools, update its plan, and decide what action to take. This illustrates different control choices; it does not establish that one is universally safer or more accurate.
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Give an agent only the tools and permissions it needs, define when it must stop, and decide which actions require human approval. More freedom to take actions makes clear guardrails and intervention points more important. OpenAI’s practical guide discusses guardrails for tool use and human intervention: OpenAI’s guide to building agents.
Start with a single agent and add tools or instructions incrementally. A single agent is simpler to evaluate and maintain; multiple agents are not automatically a better architecture.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When are multiple agents justified?
Split work across agents only when separate roles materially improve the system. Potential signals include complex conditional logic that is becoming hard to manage, unreliable tool selection despite clearer tool definitions, or a demonstrable benefit from separating prompts and tools. Multiple agents add orchestration overhead and make the system more complex.
When splitting, choose who owns the response. In a handoff, control passes to a specialist that owns the next response. With agents as tools, a manager calls bounded specialists and remains responsible for synthesizing the result. OpenAI’s orchestration guide describes these patterns and recommends splitting when it improves capability or policy isolation, prompt clarity, or trace legibility: OpenAI Developers’ orchestration and handoffs guide.
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- Map the process. List its steps, branches, inputs, outputs, and failure paths. If you can specify these reliably, begin with a workflow.
- Mark the judgment calls. If interpretation is needed at only a few known points, test bounded LLM steps while leaving sequencing and validation in code.
- Identify decisions that must adapt. Consider an agent for the portion where the system must choose tools or revise its next action based on new information.
- Set permissions and stopping conditions. Limit available tools and define approval points before allowing consequential actions.
- Evaluate the simplest viable design. Compare task outcomes, failures, human-review needs, latency, operating cost, and maintenance on representative cases. Expand autonomy only when it produces a meaningful improvement.
- Split into multiple agents only with evidence. Use specialists when separation solves a concrete problem that one agent cannot handle cleanly.
Anthropic’s workflow-versus-agent guidance was published on December 19, 2024, and notes that tooling changes over time. Its distinction between predefined and model-directed control flow is useful for architecture decisions; check current official documentation before choosing a particular implementation framework or API: Anthropic’s article.
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