OpenSearchCon 2026: Search Becomes AI’s Retrieval Layer

Key Highlights

• OpenSearch is positioning itself as the retrieval and context layer between enterprise information and AI models.
• Search should happen before model reasoning when an application needs exact facts, identifiers, permissions, or current business information.
• Retrieval quality is now part of AI quality. Wrong, incomplete, stale, or unauthorized results can cause a model to produce a confident but incorrect answer.
• OpenSearch is expanding into agent memory, observability, evaluation, and governance, but orchestration remains a missing link.
• Vendor independence, sovereignty, and cost are becoming more important as enterprises move AI workloads into production.

The News

OpenSearchCon North America brought the OpenSearch community together in San Jose from September 22 through September 24. The event showed how the project is expanding beyond its traditional role in search, log analytics, and observability to support retrieval and context for AI applications.

The OpenSearch Software Foundation also announced new General Members Adelean, Intel, and Sidecar, along with certified long-term support providers Seacom and Sidecar. The Foundation reported that OpenSearch downloads increased 140% year over year to more than 2.4 billion, while the number of project contributors grew 31%.

Separately, research from the OpenSearch Software Foundation and Linux Foundation Research found that 83% of organizations run or plan to run AI workloads. Sixty-two percent already use OpenSearch in AI workloads, while 77% consider it a core or supporting part of their AI infrastructure. Hybrid search, which combines keyword and semantic retrieval, was the preferred approach for 68% of respondents.

Analyst Take

Search may sound like a familiar technology, but its role changes when a model or agent depends on it to make a decision. OpenSearch is increasingly being positioned as the infrastructure that finds the information an AI system needs before the model reasons or acts. HyperFRAME Research Lens data found that 79% of organizations plan to use retrieval-augmented generation within the next 12 months, making retrieval quality an increasingly important part of enterprise AI architecture.

If a user asks an AI application about a product, the model should not be expected to generate an exact merchant ID from memory. The application should retrieve the authorized, current identifier from a trusted system and give it to the model as context. The model can then interpret the information or decide what to do next.

This is a better division of labor. Models are useful for understanding language, identifying intent, and working through ambiguity. Search systems are better suited to locating exact records, enforcing filters, and returning current information. Asking the model to perform both functions increases the likelihood of an incorrect answer.

However, retrieval does not make an AI application accurate by itself. If the search returns the wrong product, an outdated policy, incomplete context, or information the user was not authorized to access, the model may still produce a polished answer based on faulty evidence. Retrieval quality therefore has to be evaluated as part of answer quality.

Teams need to understand what was retrieved, why it was selected, whether the user was authorized to see it, and how the evidence affected the final response. Conventional infrastructure metrics such as uptime and latency will not show whether a healthy retrieval pipeline is returning the wrong information.

The Missing Link Between a Question and a Search

The unresolved problem is how an application decides what to retrieve.

A developer can build a service that recognizes a request for product information, searches the correct OpenSearch index, applies the necessary permissions, retrieves the merchant ID, and then sends that context to the model. But those decisions have to be designed somewhere. The application needs to know which source to search, how to construct the query, what filters to apply, and when the evidence is sufficient.

This problem extends to the business user. Someone asking an AI assistant about sales performance should not need to know which index contains the data or whether the application should use keyword, semantic, vector, or hybrid search. The user expects the system to understand the question and locate the right information.

Developers will likely build services, agents, or orchestration logic that perform this translation. The question is whether every organization must assemble that logic separately for each application and business process. OpenSearch can provide retrieval, storage, memory, and observability, but enterprises still need a reusable layer that translates business intent into the correct retrieval strategy.

Without that layer, organizations risk creating isolated AI use cases that each have their own prompts, retrieval logic, permissions, and evaluation methods. That approach becomes difficult to govern and expensive to maintain as the number of applications grows.

Memory, Observability, and AI Operations

OpenSearch is also developing a broader role in agent memory and AI operations. Its memory capabilities can store conversation history, prior actions, summaries, and other context that agents may need across tasks. This could give enterprises more control over where agent memory resides and how long it is retained.

Memory introduces many of the same challenges as retrieval. Enterprises need to determine what should be remembered, who can access it, when it should expire, and whether incorrect information can be corrected or removed. More memory does not necessarily make an agent better. Poorly governed memory can preserve errors and expose sensitive information across interactions.

Observability becomes important across the entire path. Enterprises need visibility into ingestion, indexing, embeddings, ranking, retrieved evidence, model responses, tool calls, latency, and cost. They also need to connect those technical measures to business outcomes. An application can be available, fast, and still consistently return the wrong answer.

Sovereignty and Cost

The Foundation’s emphasis on vendor neutrality also fits broader enterprise concerns about AI sovereignty. Its research found that 71% of organizations consider the ability to deploy infrastructure independently of a particular vendor or cloud provider a strategic priority. Total cost of ownership and security and compliance were the two most frequently cited infrastructure requirements.

OpenSearch gives organizations an open-source option that can run across different environments. That can provide more deployment choice and reduce dependence on a single cloud service. It does not eliminate operational responsibility. Enterprises that want greater control may also assume more responsibility for capacity planning, upgrades, security, performance tuning, and support.

Cost will depend on architecture and workload behavior. Retrieval can reduce unnecessary model calls by finding exact information before invoking an LLM, but indexing, embedding, storing memory, reranking, and monitoring also consume resources. Organizations should measure the cost of the full retrieval and generation path rather than treating search as a fixed background expense.

Looking Ahead

The most important takeaway from OpenSearchCon is that retrieval is becoming part of the execution path for enterprise AI. Models will continue to improve, but they will not contain an organization’s current product records, permissions, policies, customer history, or operating context. That information must be found and supplied when it is needed.

OpenSearch has a credible opportunity to become the retrieval infrastructure that supports that process. Its existing strengths in search, scale, observability, and flexible deployment give it a useful foundation.

The next challenge is making retrieval easier to operationalize. Developers need reusable ways to translate user intent into searches, enforce authorization, evaluate relevance, and trace a model answer back to its evidence. Business users need those decisions to happen without understanding the underlying retrieval architecture.

Search before the model is the right principle. The next stage of the market will determine who makes that principle work reliably across applications, agents, and business workflows.