Agentic Workflow vs AI Agent
In an agentic workflow, model calls and tools follow code paths the developer defined in advance, such as a fixed chain or a router. In an AI agent, the model itself decides which steps and tools to use, in a loop, until the task is done, gaining flexibility at the cost of predictability and spend.
Definitions
- Agentic Workflow PTL-0064
- An agentic workflow is a system in which language models and tools are orchestrated through predefined code paths, as opposed to an agent that chooses its own steps.
- AI Agent PTL-0063
- An AI agent is a system in which a language model dynamically directs its own process and tool use in a loop, deciding what actions to take based on environment feedback until a task is complete.
Differences
| Agentic Workflow | AI Agent | |
|---|---|---|
| Who decides the steps | The developer's code | The model |
| Predictability | High | Lower |
| Best for | Tasks that decompose cleanly in advance | Open-ended tasks whose steps cannot be known ahead |
| Common forms | Prompt chaining, routing, parallelization, orchestrator-workers | A tool-calling loop such as ReAct |
Which to use
- Prefer a workflow whenever the task can be broken into known steps.
- Use an agent when the path depends on what is discovered along the way.
Sources
- Anthropic (2024). Building effective agents.
- Anthropic (2024). Building effective agents.
Cite this page
Protologue. (2026). Agentic Workflow vs AI Agent. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0). https://protologue.com/compare/agentic-workflow-vs-ai-agent/