5 Common Problems in Deploying LLM Agents
In this video, Sam Witteveen discusses five common problems encountered when deploying LLM (Large Language Model) agents into production. These issues include reliability, excessive loops, tool failures, lack of self-checking mechanisms, and lack of explainability. Witteveen emphasizes the importance of creating reliable agents that can consistently produce useful outputs without human intervention. He highlights the problem of agents getting stuck in loops, often due to failing tools or repetitive tasks, and suggests implementing step limits to mitigate this. Customizing tools to fit specific use cases and ensuring they provide useful feedback to the LLM is also crucial.
Witteveen also stresses the need for self-checking mechanisms where agents can verify their outputs, such as running unit tests for generated code or checking the validity of URLs. Explainability is another key aspect, where agents should provide citations or logs to help users understand the reasoning behind their outputs. Finally, he touches on the importance of debugging and having intelligent logs to trace where an agent’s reasoning might have gone wrong. Witteveen concludes by encouraging viewers to assess their agents for decision points and reliability, and to consider using frameworks like LangGraph or custom Python code for more control.