Sponsored by Elastic
Unlocking the Power of Proprietary Data is Essential for Effective Agentic AI Deployment
Agentic AI-intelligent systems capable of autonomously gathering tools, data, and information to generate solutions-has become a buzzword across industries. However, the true challenge lies in ensuring these AI agents operate with precise and relevant context. In most enterprises, critical contextual data is dispersed across a myriad of unstructured sources such as emails, documents, CRM systems, and customer insights, making it difficult for AI to access and utilize effectively.
Looking toward 2026, the ability to seamlessly integrate and leverage this scattered data will be a decisive factor in accelerating the adoption of agentic AI worldwide, according to Ken Exner, Chief Product Officer at Elastic.
“The foundation of successful agentic AI is relevant data,” Exner emphasizes. “When AI acts autonomously on your behalf, the accuracy of its decisions hinges on the quality and pertinence of the information it accesses. In my experience, when AI projects falter, it’s almost always due to a lack of relevant context.”
Widespread Adoption of Agentic AI on the Horizon
The race to harness agentic AI is intensifying as organizations seek competitive advantages and operational efficiencies. A recent Deloitte forecast predicts that by 2026, over 60% of large enterprises will have fully integrated agentic AI into their operations, transitioning from pilot programs to widespread use. Complementing this, Gartner projects that by the end of 2026, 40% of enterprise software will embed task-specific AI agents, a dramatic rise from under 5% in 2025. This shift marks the evolution of AI assistants into sophisticated, context-aware agents capable of specialized functions.
Introducing Context Engineering: The Backbone of Agentic AI
At the heart of enabling agentic AI to function effectively is the discipline of context engineering-the strategic process of delivering the right data to AI agents at the right moment. This ensures that large language models (LLMs) not only receive accurate information but also understand which tools to deploy and how to interact with APIs to retrieve and process that data.
While open-source protocols like the Model Context Protocol (MCP) facilitate communication between LLMs and external data sources, few platforms offer an integrated environment that combines data retrieval, governance, and orchestration tailored to proprietary enterprise data.
Elastic has long been a pioneer in this space, providing a robust foundation for context engineering through its Elasticsearch platform. Recently, Elastic introduced Agent Builder, a powerful new feature designed to streamline the entire lifecycle of AI agents-from development and configuration to execution, customization, and monitoring.
Agent Builder empowers users to construct MCP-compliant tools that operate on private datasets using advanced methods such as Elasticsearch Query Language (EQL), a versatile piped query language for filtering, transforming, and analyzing data, alongside workflow modeling. By combining these tools with tailored prompts and LLMs, organizations can create highly specialized AI agents.
One standout capability of Agent Builder is its ready-to-use conversational agent, which enables users to interact directly with indexed data through natural language queries. Additionally, it offers the flexibility to build custom agents from the ground up, leveraging private data and bespoke prompts.
“Data is central to everything we do at Elastic,” Exner notes. “With Agent Builder, you simply connect it to an Elasticsearch index, and instantly you can engage in meaningful conversations with any data you’ve indexed-or even data from external systems integrated through our platform.”
Context Engineering: Emerging as a Distinct Discipline
As AI continues to evolve, context engineering is rapidly becoming a specialized field. While it doesn’t require advanced computer science credentials, mastering it involves understanding best practices and developing an intuitive sense for crafting effective prompts and data flows.
“Our goal is to simplify this process,” Exner explains. “The key question organizations must answer is how to harness AI-driven automation to boost productivity. Those who focus on this will gain a significant edge.”
The field is already witnessing the emergence of new methodologies. The industry has progressed from basic prompt engineering to retrieval-augmented generation-where external information is fed into the LLM’s context window-and now to MCP-based solutions that enable LLMs to select and use appropriate tools. Yet, this is just the beginning.
“Given the rapid pace of innovation, I’m confident that novel approaches will continue to surface,” Exner predicts. “Future patterns will enhance how data is shared with LLMs, ensuring they remain grounded in accurate, private information-even data they weren’t originally trained on.”
Agent Builder is currently available as a technical preview. Explore the platform and access comprehensive documentation to begin building your own AI agents today.
