13% to 43% accuracy
Your AI agent works perfectly… until the tool list gets too long. The fix? Smarter filtering before it even sees the options.
What the video says
Imagine an AI agent that works flawlessly in demos—until it doesn't, not because the model changed, but because the tool list grew too long. By the time it hits 40 tools, the same agent that nailed every test now hallucinates parameters, calls the wrong API, or stalls mid-task waiting on a call it should never have made.
Research shows agent accuracy degrades measurably once tool counts pass just 10 to 15, and And the RAG-NCP paper reveals why: exposing the full catalog to the model drops tool selection accuracy to a staggering 13.62%. The fix doesn't require a bigger model, just a smarter view of what the model sees before it acts, and the numbers prove it.
Semantic retrieval triples accuracy to 43.13%, while cutting prompt tokens by over half. 6 techniques form the layers: Gating filters out turns that need no tool at all.
Retrieval and routing narrow the catalog to what's relevant. Planning sequences, multi-step tasks, and fallback logic catches the cases where the first attempt misses.
Benchmarking closes the loop, turning guesswork into measurable progress, because without it, you're relying on spot checks instead of knowing whether a change actually helped or just felt better.
