Elastic Introduces Agent Builder to Speed Up AI Agent Development on Your Data
Elastic announced Agent Builder, a set of Elasticsearch-powered capabilities that helps developers build custom AI agents on company data in minutes. It also ships with a native conversational interface so teams can explore, analyze, and optimize any data already in Elasticsearch.
The pitch is simple: as AI agents take on more complex, data-heavy work, they need precise context from the right sources at the right time. Agent Builder streamlines the full lifecycle-development, configuration, execution, customization, and observability-directly inside Elasticsearch.
Why it matters
Most orgs have context scattered across documents, emails, apps, tickets, and customer feedback. Getting the right snippets into an agent at the right moment is the difference between helpful and hallucinated. Agent Builder treats context engineering as a first-class workflow, combining retrieval, governance, and orchestration in one place.
Key capabilities
- Chat with company data out of the box: A built-in conversational agent lets you query any Elasticsearch data in natural language and immediately get useful responses.
- Relevance-focused built-in tools: It auto-selects the right index, understands the schema, converts natural language into optimized semantic, hybrid, or structured queries, and returns only the most relevant context to the LLM.
- Custom tools with ES|QL: Define tools that use ES|QL for precise control over which data is pulled, how it's filtered, and what context is sent-improving accuracy and security.
- Custom agents: Set a system prompt, choose allowed tools, and configure a security profile that fits your org's needs.
- Interop with MCP and A2A: Connect external agents and applications over the Model Context Protocol and A2A while keeping governance in the Elasticsearch execution layer.
How to get started in minutes
- Point Agent Builder at your Elasticsearch indexes. Confirm schemas and field types are discoverable.
- Use the built-in conversational agent to ask questions and validate retrieval quality.
- Add custom tools with ES|QL to shape context windows, enforce filters, and control freshness.
- Create a custom agent: define its role, set guardrails, restrict tool access, and tie it to a security profile.
- Measure outcomes with built-in observability and iterate on prompts, tools, and filters.
Practical use cases for IT and engineering teams
- Ops and support: Turn tickets, runbooks, and KBs into accurate answers with links back to sources.
- Incident response: Query logs, metrics, and prior incidents to speed triage and recommended actions.
- Developer enablement: Q&A across code snippets, API docs, and architecture notes stored in Elasticsearch.
- Product and CX analysis: Mine feedback, NPS comments, and CRM notes to summarize issues and trends.
- Security investigations: Ask targeted questions across alerts and detections with strict data access controls.
Technical notes that matter
- Context delivery: Relevance selection, index routing, and query translation are handled natively.
- Query strategy: Natural language converts to semantic, hybrid, or structured searches based on data shape.
- ES|QL tools: Give agents precise, auditable access paths to data; keep queries reproducible.
- Governance: Security profiles and execution controls stay within Elasticsearch.
- Observability: Built-in visibility helps tune prompts, tools, and retrieval for accuracy and cost.
Availability
Agent Builder is available today in Technical Preview on Elastic Cloud in serverless, with availability coming in version 9.2. To see it in action or start building, visit the Elastic blog.
About Elastic
Elastic (NYSE: ESTC) is the Search AI Company. Its Search AI Platform supports search, observability, and security solutions used by thousands of companies, including more than half of the Fortune 500. Learn more at elastic.co.
If you're leveling up your agent skills-prompt design, retrieval strategies, and evaluation-browse practical resources at Complete AI Training: Prompt Engineering.
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