What is the ReAct pattern?
The ReAct pattern (Reasoning and Acting) is a way of running a language model as an agent by interleaving three kinds of output: a thought, in which the model states what it knows and what it needs next; an action, in which it calls a tool with specific arguments; and an observation, in which the tool’s result is appended to the context. The model then writes the next thought, and the cycle repeats until it decides the task is done and writes a final answer.
The pattern originated in academic work that combined chain-of-thought prompting with tool use. Chain-of-thought alone lets a model reason but gives it no way to check facts. Tool use alone lets it act but with no visible plan. ReAct joins the two, so every action has a stated reason and every reason is tested against a real result. Most agent frameworks implement some version of it.
Why the ReAct pattern matters
For a business, the pattern’s main value is legibility. A ReAct trace reads like a work log: “The customer asked about invoice 4471. I need the invoice status. Calling get_invoice(4471). Result: paid on the 3rd, check number 2290. The question is answered.” A support lead can read that and see exactly where a wrong answer came from. The same trace feeds the AI audit log and the decision traceability that regulated companies need.
The pattern also fails in recognizable ways. A model can reason itself into a loop, calling the same tool repeatedly with slightly different arguments, or it can state a confident thought that the observation does not support and carry on anyway.
ReAct pattern in practice
A managed service provider runs a triage agent on incoming tickets. For a ticket that reads “VPN down for the whole Portland office”, the trace shows the agent reasoning that a site-wide outage points to the firewall, calling the monitoring tool for the Portland firewall, observing a failed health check from ten minutes earlier, concluding that this matches, and then creating a priority-one incident linked to the device. The technician who picks it up sees the chain of reasoning and the evidence, and the ticket is already categorized correctly.
What to watch for
- Cap the number of thought-action cycles per task, and route to a person when the cap is hit.
- Treat the model’s thoughts as useful evidence rather than proof; the observation from the real system is the part to trust.
- Store the full trace, since it is the cheapest form of explainability an agent can provide.
- Watch for repeated near-identical tool calls, which usually mean the agent is stuck.