Elastic has moved its Agent Builder into general availability, expanding the platform’s capabilities to help enterprises build AI agents that are grounded in their own data and capable of taking reliable, context-driven actions.
The announcement marks a step beyond experimental agent frameworks toward more production-ready deployments. While interest in AI agents is surging, many organisations continue to struggle with a familiar problem: agents fail when they lack accurate context or cannot reliably interact with enterprise systems. Elastic is positioning Agent Builder as an answer to that gap.
Built on Elastic’s Elasticsearch platform, Agent Builder focuses on what the company calls “context engineering” — the ability to retrieve, rank, and analyse enterprise data at scale so agents can reason more accurately. The platform brings together data ingestion, retrieval, built-in and custom tools, conversational interfaces, and agent observability in a single workflow. According to Elastic, developers can either chat directly with enterprise data or spin up custom agents within minutes.
A key part of the release is native support for Model Context Protocol (MCP) and agent-to-agent (A2A) protocols, which simplifies deployment into existing AI ecosystems. This includes integrations with Microsoft’s Foundry and Agent Framework, enabling developers to use Elasticsearch as a core knowledge source while running agents within Microsoft’s AI stack.
Industry partners echoed the focus on moving agents from demos to dependable systems. Several pointed to the complexity of connecting large language models to enterprise tools and unstructured data as a major reason agentic systems struggle in real-world environments. By standardising how agents retrieve context, reason, and act, Elastic is aiming to reduce that friction.
From reasoning to action with Elastic Workflows
Alongside Agent Builder, Elastic also unveiled Elastic Workflows in technical preview. The new capability is designed to address another enterprise pain point: execution reliability.
Many current agent frameworks rely on LLMs to plan and manage every step of an automation. While flexible, that approach can be brittle, especially for business-critical processes where predictability matters. Elastic Workflows introduces a rules-based orchestration layer that allows agents to trigger actions across internal and external systems with greater consistency.
In practice, this means agents can reason using AI, but hand off execution to deterministic workflows for tasks such as data transformation, system updates, or integrations. The combination is intended to give enterprises the balance they often seek — intelligent decision-making without sacrificing control or auditability.
Aiming for enterprise-scale adoption
Elastic says agents built with Agent Builder remain model-agnostic, allowing organisations to work with hyperscalers and managed model-as-a-service providers without being locked into a single vendor. This flexibility is increasingly important as enterprises experiment with multiple models and architectures.
With general availability, Elastic is signalling that agentic AI is moving out of the lab and into operational environments. The addition of Workflows suggests a recognition that reasoning alone is not enough — enterprises need agents that can act predictably within governed systems.
As organisations look to scale AI beyond pilots, platforms that combine enterprise context, observability, and reliable automation are likely to play a central role. Elastic’s latest release positions Agent Builder as part of that emerging foundation.