Agent executor, ReAct and toolkits
An Agent Executor runs an Agent loop: Reasoning chooses an Action, a Tool runs, an Observation returns, and Decision Making selects the next step.
In plain terms
ReAct means Reason -> Act -> Observe. The model reasons about what to do, chooses a tool call, your code runs it, then the observation goes back into the next model call.
Why it matters
Agents need a runtime that keeps looping until completion, handles tool errors and stops runaway behavior. LangChain's Agent Executor is the classic wrapper around that loop.
How it works
Define Tool objects or a Toolkit, give them names, descriptions and schemas, then let the agent choose. The Agent Executor tracks the scratchpad, calls tools, appends Observations, enforces iteration limits and returns the final answer.
When you use it
Use it for search agents, database assistants, coding helpers, analysis workflows and any task where the next action depends on the previous result.
Common mistakes
- Giving the agent too many overlapping tools.
- Letting it run forever without step limits.
- Confusing hidden Reasoning with guaranteed truth.
Best practices
- Keep tools specific and well-described.
- Validate every Action before side effects.
- Trace each Observation so failures are explainable.
Try it yourself
Create a small Toolkit with calculator and document-search tools, then run a ReAct agent through an Agent Executor with a five-step limit.