Elastic Launches Agent Builder for Enterprise AI Agents
Elastic (NYSE: ESTC) has introduced Agent Builder, a focused toolkit for developers building secure, reliable, context-driven AI agents. The goal is straightforward: speed up development, improve accuracy on enterprise data, and ship agents you can trust in production.
What Agent Builder delivers
- AI agent development: Build agents that read enterprise data, keep context, and take actions with guardrails.
- Context-driven intelligence: Built on Elasticsearch, it extracts signal from unstructured sources so agents reason with relevant context and reduce wrong answers.
- Workflow functionality: With Elastic Workflows (tech preview), agents can execute actions across systems with consistent, traceable steps-closing the reliability gap in AI automation.
Why this matters for engineering teams
Most pilots fail at the "last mile" where agents must act, not just chat. Agent Builder focuses on that gap with context retrieval and controlled execution. Less glue code, fewer brittle integrations, and clearer paths to production.
- Faster path from prototype to production on your existing Elastic stack.
- Security and governance aligned to your data and permissions.
- Reliability through defined workflows and observable agent behavior.
Context that actually helps the agent
Agents need the right slice of context at the right time. Agent Builder uses Elasticsearch to pull relevant information from unstructured data-documents, tickets, logs, and more-so outputs map to reality, not guesswork. The result: better answers, fewer escalations, and cleaner handoffs to downstream systems.
Workflows: from intent to action
Elastic Workflows (tech preview) lets agents perform multi-step tasks consistently across systems. You get predictable execution instead of ad-hoc API calls.
- Open a service ticket with linked evidence and status updates.
- Enrich a CRM record and notify the owner with a summary.
- Draft a knowledge base update and route it for approval.
Availability
Agent Builder is available now in Elastic Cloud Serverless and included in the Enterprise Tier of Elastic Cloud Hosted. Existing customers can access it immediately to accelerate AI agent development.
Explore Elastic for platform details: elastic.co. If you're new to the stack, this overview is useful: What is Elasticsearch.
Good starting use cases
- Knowledge assistant for support or field engineering.
- Ops triage aide that summarizes alerts and proposes next steps.
- Research agent that compiles context packs for analysts or product teams.
Implementation tips
- Scope access tightly. Give agents the minimum data and actions needed.
- Keep steps explicit. Use clear tools and approvals for high-impact actions.
- Log everything. Track prompts, context, decisions, and outcomes for audits.
- Ship small. Start read-only, measure quality, then enable writes with checks.
Next steps
Pick one workflow, define success metrics (accuracy, action success rate, time saved), and iterate. With context retrieval and workflow execution handled, your team can focus on the parts that move the needle: data quality, prompts, and guardrails.
Want structured upskilling on agent patterns and automation? See our curated programs by role: Complete AI Training - Courses by Job.
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