AI agents rely on LLM reasoning or vector similarity to decide and 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.

With this, Agentic observability has emerged as a critical discipline in 2026, shifting from simple logging to in-depth tracing of non-deterministic, multi-step workflows. Building a reliable GenAI app monitoring strategy for tracing the reasoning chain, now becomes more important from an observability pov.

In this talk, we will demonstrate how we can understand AI Agent decisioning for tool calling and further reasoning with OpenSearch. We will showcase how OpenSearch indexes agent logs, tool call metadata, execution constraints and past outcomes.

We will showcase a live hands-on demo of OpenSearch ML, indexing the AI Agent logs-metrics and further use them to comprehend the logs to understand the functioning and reasoning of AI agents.

Confused about how AI Agents are making decisions ? Then this talk is for you !