A common issue in the development of AI agents is their tendency to perform well in demos, but fail when exposed to real-world users. After building automation systems for numerous clients, it has become apparent that the primary causes of failure are not related to the choice of Large Language Model (LLM). Instead, the failures can be attributed to three key factors:

• Bad chunking in RAG pipelines: The way documents are split into chunks can significantly impact the performance of the AI agent. If the chunks do not preserve context across sentences, the retrieval will be mediocre, regardless of the vector database used.
• Prompts written for demos, not edge cases: Demo inputs are typically clean and well-structured, whereas real-world user inputs can be vague, incomplete, or even intentionally broken. If the prompts are not stress-tested with bad inputs, they will likely fail in public.
• No fallback logic: When the AI agent is confused or unsure, it needs a fallback mechanism to handle the situation. Without this, the agent may either provide confident but incorrect responses or return nothing at all, both of which are undesirable outcomes.

These issues highlight the importance of focusing on the scaffolding and infrastructure surrounding the AI model, rather than solely blaming the model itself. By addressing these common pitfalls, developers can create more robust and reliable AI agents that perform well in real-world scenarios.

Photo by Md Jawadur Rahman on Pexels
Photos provided by Pexels