What it is
An agentic workflow wraps a language model in a loop so it can pursue a goal over many steps. Instead of answering once, the model plans, takes an action through a tool, observes the result, and decides what to do next.
The act observe loop
The core pattern repeats a few stages until done.
- The model reasons about the current state and the goal
- It chooses an action, often a tool call
- It reads the observation returned by the environment
- It updates its plan and loops, stopping when the goal is reached
This reason then act cycle is the engine behind tool using assistants that browse, run code, or edit files across many turns.
Designing them well
Open ended loops can drift, so good designs add structure.
- Memory lets the agent carry context across steps without overflowing the prompt
- A planner can break a big task into subtasks, sometimes handled by separate agents
- Guardrails like step limits and human approval keep a misbehaving loop in check
The trade off is autonomy versus reliability. More freedom solves harder tasks but raises the chance of costly mistakes.
Key idea
An agentic workflow loops a model through reasoning, action, and observation so it can pursue multi step goals with tools.