Agentic Workflow
A structured pipeline where an LLM makes decisions at defined branch points, but the overall flow is deterministic.
Agentic workflows sit between rigid automation (if-this-then-that) and fully autonomous agents. The developer defines the steps and the branching logic; the LLM decides which branch to take, extracts information, or generates content at each step.
This pattern trades some flexibility for a large gain in reliability, cost predictability, and debuggability. Most 'AI agents' shipped in production today are actually agentic workflows.
LangGraph, Temporal, and the OpenAI Agents SDK are common orchestration layers. The key design choice is where to give the LLM freedom and where to constrain it.
Key points
- Higher reliability than open-ended agents
- Easier to price, monitor, and rate-limit
- Well-suited to business processes with defined steps
- Debuggability comes from making every LLM call inspectable
Common use cases
Frequently asked
Related terms
An LLM-driven system that plans, calls tools, and executes multi-step tasks autonomously.
An open standard that lets LLMs discover and call external tools, data, and services in a uniform way.
The practice of designing model inputs to produce reliable, task-specific outputs.
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