How to tell when a predictable workflow is enough and when an agentic approach is worth the extra cost and complexity.
Two different designs
In a workflow, the developer defines the sequence of steps and the model performs specific tasks within them. In an agent, the model decides the sequence itself. Both can use LLMs; the difference is who controls the path.
Workflow patterns
Common LLM workflow patterns include prompt chaining (one step feeds the next), routing (classify the input, then send it to the right handler), parallelisation (run several checks at once), and evaluator loops (generate, check, revise). They are predictable, easy to test and cheap to run.
Where workflows win
Invoice processing, data extraction, report generation, content moderation and most back-office automation follow known steps. A workflow gives consistent results, clear logs and straightforward debugging. It is usually the right starting point.
Where agents win
Agents earn their cost when the number and order of steps cannot be known in advance: open-ended research, diagnosing an unfamiliar problem, or handling requests that span several systems in unpredictable combinations.
Five questions to decide
Can you draw the process as a flowchart? How many tools might be needed? What does a wrong action cost? Can a person review before anything irreversible happens? How much do inputs vary? Mostly predictable answers point to a workflow; high variability with manageable risk points to an agent.
The hybrid approach
Many production systems are workflows with one agentic step, for example a fixed intake and approval process with an agent that investigates unusual cases. This keeps most behaviour predictable while handling the cases rules cannot.
Trade-offs to plan for
Agents generally use more tokens, take longer and produce more varied results, which makes testing harder. Plan for step limits, cost limits per task, detailed tracing and a clear handover to a person when the agent is unsure.
Key takeaways
- Workflows: the developer controls the path. Agents: the model does.
- Start with a workflow when the steps are known.
- Use agents where variability is real and risk is manageable.
- Hybrid designs are often the most practical choice.
If you are deciding how much autonomy an automation needs, our Agentic AI Engineering team can help you design the right mix.
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- A practical guide to building agentsOpenAI · 2025 · Guide (PDF)cdn.openai.com