What 1,192 conversations taught us about knowledge base search in AI agents
A recent analysis of 1,192 conversations revealed the significance of knowledge base search in AI agents. The search tool was frequently used as a fallback for questions that native tools could not address. Additionally, it provided context for native tools and helped the agent determine the appropriate actions to take based on user inquiries.
- ▪The search_knowledge_base tool was the most utilized, nearly matching the combined usage of all native tools.
- ▪32.1% of conversations involved users asking questions that native tools could not answer, highlighting the knowledge base's role as a fallback.
- ▪The knowledge base not only contextualized answers from native tools but also assisted the agent in determining which tools to use.
Opening excerpt (first ~120 words) tap to expand
I'm Finn, co-founder of Kapa - we build customer-facing AI assistants on top of technical documentation.A few months ago we shipped an agent inside our own product. It lives in our web app and our customers use it to ask questions about their deployment - things like "how many Slack bot questions have users asked in the last month?". We built the agent because the analytics tooling we'd shipped (clustering, tagging, filters) never quite covered every use case, and we wanted to see if a chat interface could.For context, the agent has around native 30 tools to interact with our platform like search_conversations, display_chart, and so on.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Kapa.
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