OpenSearchCon India 2026

Modern AI agents generate massive operational telemetry: prompts, tool executions, policy decisions, and execution outcomes. Without strong indexing and analytics, debugging and governing these systems becomes extremely difficult.

This talk presents a practical architecture for operating autonomous systems at scale using OpenSearch as the observability and decision-intelligence backbone.

We explore an AgentOps architecture where OpenSearch indexes agent traces, policy decisions, incident telemetry, and execution evidence to enable real-time debugging, safety enforcement, and auditability.

Topics covered include building searchable “decision traces” for AI agents, designing observability pipelines for autonomous systems, using OpenSearch for policy audits and incident forensics, and scaling analytics for multi-agent operations.

Attendees will learn concrete patterns for using search and analytics infrastructure to make AI-driven systems observable, debuggable, and governable in production.