LLMs are powerful AI models, but they can hallucinate, go out of context, or simply provide the wrong output. You need to take their output and have layers of verifications against it so that you only let the right data in. The verification layers must send the feedback for the next iteration.
The important question then becomes when to stop if it ends up in a never ending cycle? Do you stop at a certain number of retries and take the best out of all the failed outputs? or Do you keep it running until it passes a certain threshold?
It very much depends on the problem you are trying to resolve.
What are your thoughts and experiences about building iterative agentic patterns?
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Iterative AI agents often falter due to a lack of deterministic guardrails, which can lead to unpredictable outputs. In my experience with enterprise teams, combining retrieval-augmented generation (RAG) with model monitoring tools like LangChain provides a structured approach to mitigate this. By setting clear boundaries and continuously refining query results, you can enhance reliability and contextual accuracy. It's not just about the model, but how you frame and guide its responses. - Ali Muwwakkil (ali-muwwakkil on LinkedIn)