What makes a workflow "agentic"
The term agentic distinguishes workflows where AI agents have autonomy — they can decide what tool to use, what information to gather, and how to structure their output — from simple prompt-response patterns where a human drives every step.
In a traditional AI interaction, a user sends a prompt and receives a response. In an agentic workflow, the user defines an objective and the system executes a multi-step pipeline to achieve it.
Key characteristics:
- Autonomy — agents decide how to accomplish sub-tasks
- Sequential execution — output from one stage feeds the next
- Tool use — agents can call external APIs, search databases, or run verification checks
- Error handling — agents can retry, escalate, or route around failures
How it differs from prompt chaining
Prompt chaining is a simpler pattern: the output of one LLM call becomes the input to the next, with fixed logic at each step. Agentic workflows go further — agents can branch, loop, use tools, and make conditional decisions based on intermediate results.
| Feature | Prompt Chaining | Agentic Workflow |
|---|---|---|
| Decision-making | Fixed logic | Agent decides |
| Tool access | None or limited | Full tool use |
| Error recovery | Fails or retries blindly | Adaptive |
| Branching | Linear | Conditional |
Common stages in an agentic workflow
A typical agentic workflow for artifact production includes:
- Intent capture — user provides sources, topic, or parameters
- Research — agents gather and organize source material
- Analysis — agents extract claims, identify key themes, cross-reference
- Drafting — agents produce structured content based on analysis
- Verification — agents check citations, validate claims, run QA
- Output — final artifact is formatted and delivered
The power of agentic workflows isn't any single stage — it's the composition. Each step informs the next, producing output that's more coherent and defensible than any one-shot generation.
Why it matters
One-shot generation (single prompt → single response) works for simple tasks. But for anything requiring research depth, source verification, or structured output, the error rate compounds. Agentic workflows break the problem into manageable stages where each can be verified independently.
This is particularly important for:
- Citation-heavy content — where hallucinated sources are unacceptable
- Batch processing — where consistency across hundreds of artifacts matters
- Regulated domains — where audit trails are required