I am building a research agent using LangChain, but it often loops indefinitely or makes up facts when searching the web. I've heard the ReAct (Reason + Act) framework can help, but I'm struggling with the implementation. How do you structure the prompt to ensure the agent actually verifies its own 'Thoughts' before taking an 'Action'?
3 answers
The secret to a stable ReAct agent is a very strict "Observation" loop. In my experience, you need to explicitly tell the agent in the system prompt: "If the search result is null or irrelevant, do not invent data; instead, refine your search query." We found that adding a "Self-Correction" step between the 'Thought' and 'Action' phases reduced hallucinations by nearly 40%. For example, we ask the agent to rate its own confidence in the planned action on a scale of 1-5. If it is below a 4, the agent is forced to look for a different tool or re-read the previous observation. This creates a more grounded "chain of thought" that is much more reliable for production environments.
Are you finding that the looping issue is related to the context window limit of the model you’re using, or is the agent just getting stuck in a repetitive logic cycle?
Try using the "Plan-and-Execute" pattern instead of a pure ReAct loop. It forces the agent to create a full roadmap before it starts clicking buttons, which keeps it focused on the end goal.
Kimberly is spot on. Planning first reduces the "distraction" that agents face when they encounter unexpected data mid-stream. It’s a much more robust architecture for complex tasks.
To answer your question, Gregory, it's usually a logic cycle. To fix this, I implemented a "Global Step Counter." If the agent exceeds 5 steps without a "Final Answer," I force a hard break and have a separate "Supervisor" agent review the logs to find the bottleneck. This prevents the agent from burning through API tokens in a recursive loop. It also allows the supervisor to provide a "hint" back to the original agent, effectively acting as a human-in-the-loop without the actual human.