AI agents often rely on LLM reasoning or vector similarity to choose tools, but these approaches lack deterministic control, explainability and low-latency guarantees. In real production systems, agents need a fast and reliable way to decide which tool to invoke based on structured rules and historical context.

In this talk, we will demonstrate how Apache Lucene can be used to understand AI Agents decisioning for tool calling and output generation. We will showcase how Lucene indexes agent logs, tool metadata, execution constraints and past outcomes. We will also show how Lucene can be used to filter, analyze and score agent tool calls at runtime.

We will show a live hands-on demo of how Apache Lucene can be used to store logs-metrics and how to comprehend these logs to understand the functioning of AI agents.

Attendees will also get GitHub templates and code samples for the complete demo implementation.