I’ve been experimenting with agentic frameworks, but I’ve noticed that as the task history grows, the agent becomes significantly slower and less accurate. Is it true that RAG systems are the primary cause of this bottleneck, or is it an issue with how we handle the context window?
3 answers
The bottleneck you're experiencing is likely due to "context poisoning" rather than just the storage itself. In many implementations, the retrieval layer pulls in too many irrelevant snippets, which then floods the LLM's context window with noise. When the model has to process 100k+ tokens just to find one relevant fact, the reasoning capabilities start to degrade. To optimize this, you should look into implementing a reranking step. This ensures that only the top 3-5 most high-fidelity chunks are actually passed to the agent, reducing both latency and the likelihood of the agent losing its original goal in the sea of retrieved data.
Do you think the issue lies more with the vector database latency or the actual semantic search accuracy during the retrieval phase?Michael Brennan
Most of these bottlenecks disappear if you use a tiered memory approach, separating short-term task history from the long-term knowledge base.
Susan makes a great point. Kimberly, segregating episodic memory from semantic memory is a standard best practice in the Machine Learning domain to keep agent performance high.
Michael, it's usually a combination of both. However, accuracy is the bigger silent killer. If your embedding model isn't fine-tuned for your specific domain, the agent retrieves semantically similar but logically useless information. This forces the agent to "hallucinate" a bridge between the user's request and the bad data it was given.