And the ReAct format.
What makes something an agent
In a workflow (Level 3) your code decides the order of steps and the model fills in each one. In an agent the model decides which step comes next , and how many steps there will be. The simplest honest definition:
Agent loop
Repeat: send the model the task and everything that has happened so far; the model replies with either a tool call or a final answer ; if it is a tool call, run it, append the result to the history, and go round again.
Three things make this work, and all three are ordinary code:
A history that grows each step (what was asked, what the model did, what came back).
A parser that turns the model's text into an action your code can run.
Limits that stop the loop when the model will not.
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The agent loop: the model proposes, your code disposes Question Model call system + question + all steps so far Parse Thought, Action, Input (JSON) Finish? Answer yes Run the tool your code, not the model no Observation text result or error message Append to history action + observation Guards that live in the loop, not in the model step limit | format errors and tool errors go back as observations | repeated identical action: nudge, then stop Every pass makes the next prompt longer, so cost grows with the number of steps. The loop: the model proposes, your code parses, runs, and appends
ReAct (Reason plus Act)
A pattern in which the model alternates a short written Thought with an Action (a tool call), your code returns an Observation , and the cycle repeats until the model chooses a final-answer action.
The example's transcript for "How many metres taller is Mount Tarn than Mount Hale?":
Thought: First quantity. Action: search {"query": "Mount Tarn"}
Observation: Mount Tarn is 4200 metres tall.
Thought: Second quantity. Action: search {"query": "Mount Hale"}
Observation: Mount Hale is 3650 metres tall.
Thought: Subtract. Action: calc {"expression": "4200-3650"}
Observation: 550
Thought: Done. Action: finish {"answer": "550"}
What each part is for:
Thought makes the choice inspectable and gives a chance to plan; it is also extra tokens you pay for.Action chooses one tool from a fixed list, plus a special finish action that ends the run.Action Input is structured (JSON), so your code can validate it.Observation is whatever the tool returned, as text. It is the model's only window on the world, so errors must be written so that the model can act on them ("No file named 'order.csv'. Files: orders.csv", not "failed").