OpenSearchCon India 2026

OpenSearch Dashboards plugins are often used to add individual features, but how far can the plugin model go? In this talk we share how we took the dashboards-observability plugin skeleton and evolved it into a full observability platform supporting tracing, RUM, profiling, synthetics, model observability, alerting, AI-powered RCA, and pipeline monitors — all within a single plugin.

We’ll walk through the architectural patterns behind this evolution, including domain-based state management, lazy-loaded modules, custom clients, and config-driven feature toggling. We’ll also discuss integrating rich visualizations such as trace timelines, dependency graphs, and flamegraphs into the Dashboards plugin shell, extending the query grammar for syntax highlighting, and organizing server routes with a middleware layer to proxy multiple backends.

Attendees will leave with practical patterns for building complex applications on OpenSearch Dashboards and a deeper understanding of how the plugin architecture can evolve from simple extensions into an extensible application platform for full solutions.

Your users search for “comfortable shoes for standing all day” and get shoe polish. 30% of searches return zero results even when products exist. This is keyword hell, costing you customers daily.

This session reveals how we transformed Adobe Commerce search using OpenSearch’s vector search capabilities. Learn the complete technical journey, including text embeddings, k-NN similarity, and hybrid search that combines keyword precision with semantic understanding.

We’ll cover the architecture, including vector indexing with FAISS and Lucene, ingest pipelines for automatic embedding generation, and query-time optimization.

Honest challenges included computational costs, embedding model selection through sentence-transformers comparisons, dimension tradeoffs between 384 and 768, cold start problems, and explainability to stakeholders.

You’ll get actionable knowledge such as exact OpenSearch configurations, PPL queries for semantic search, hybrid scoring strategies, open-source model recommendations, and cost optimization for Indian startups, with $30 per month infrastructure compared to $200 or more for managed vector databases.

Modern AI assistants and knowledge systems rely heavily on high-quality retrieval to provide accurate and contextual responses. While large language models generate answers, it is the underlying search infrastructure that retrieves relevant information and grounds AI outputs in real data.
In this session, we explore how modern AI applications use search infrastructure as the backbone of their intelligence layer. We will examine the architectural patterns that combine OpenSearch with embeddings, vector search, and hybrid retrieval to power knowledge assistants, enterprise search systems, and AI copilots.
The talk will walk through the evolution of search infrastructurfrom traditional keyword-based search to semantic retrieval and retrieval-augmented generation (RAG). We will discuss key design considerations such as document chunking strategies, embedding pipelines, hybrid ranking approaches, and relevance tuning required to build reliable AI systems.

Attendees will also learn how it enables scalable retrieval pipelines that support real-time indexing, vector similarity search, and metadata filtering—capabilities that are essential for powering AI-driven knowledge systems at scale.

Modern Kubernetes platforms generate massive volumes of logs, yet most teams still rely on keyword searches and dashboards to debug production issues. This slows down incident response and hides valuable operational knowledge inside unstructured data.

In this talk, we’ll demonstrate how to build an AI-assisted observability pipeline using Fluent Bit, OpenSearch, and Retrieval-Augmented Generation (RAG) to transform raw logs into queryable, contextual insights. We’ll walk through a cloud-native architecture that streams logs from Kubernetes workloads, enriches and indexes them in OpenSearch, and uses RAG to answer operational questions in real time.

Attendees will see how this approach helps SREs and platform teams reduce MTTR, improve root-cause analysis, and move from reactive troubleshooting to proactive observability—using open-source, CNCF-aligned tooling.

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.