What's the difference between agents and workflows?
Workflows are deterministic — same steps every time. Agents are autonomous — they decide their own steps to reach a goal.
Last updated: 2026-05-04
The core distinction
- Workflow: a predefined recipe. Step 1 → Step 2 → Step 3. Same path every time. You designed it, the AI just executes.
- Agent: a goal. "Research my competitor and write a 5-section comparison post." The AI decides which tools to call, in what order, and when it has enough info to stop.
When to use which
| Use case | Pick |
|---|---|
| Repeatable production tasks (publish daily LinkedIn post) | Workflow |
| Predictable cost / time budget | Workflow |
| Open-ended research ("find me a good niche to enter") | Agent |
| Multi-source data gathering with unknown depth | Agent |
| You want auditable, reproducible steps | Workflow |
| You want exploratory, surprising output | Agent |
Trade-offs
Workflows pros: cheap (1 model call per step), fast, predictable, debuggable. Workflows cons: rigid — if your inputs change shape, you must redesign.
Agents pros: flexible — they handle messy / open-ended tasks well. Agents cons: expensive (5–20 model calls per run), slow (1–3 minutes typical), occasionally take a wrong turn and waste tokens.
Cost example
Same goal: "Research and write blog post about renewable energy trends in 2026."
- Workflow (3 tools chained): ~$0.04, 30 seconds.
- Agent (autonomous, 12 tool calls): ~$0.50, 2 minutes.
Workflow wins when you've solved the problem and want to repeat it. Agent wins when you're still exploring.
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