Agent Builder
Elastic Agent Builder is a conversational AI platform from Elastic that allows developers to create context-driven AI agents capable of querying and acting on data stored in Elasticsearch.
Publisher review
Elastic Agent Builder is a conversational AI platform from Elastic that allows developers to create context-driven AI agents capable of querying and acting on data stored in Elasticsearch. It is designed for DevOps engineers, SREs, and application developers who need to build production-grade agents that can understand their operational data and take actions—such as triggering alerts, generating reports, or executing workflows—without leaving the Elastic ecosystem. The tool is generally available as of early 2026, with Elastic positioning it as a way to bring context engineering into standard developer workflows, enabling agents that are grounded in real-time observability and search data.
Agent Builder integrates natively with Elastic's data pipeline, providing native data preparation and ingestion, retrieval and ranking, and a tool registry that includes both built-in and custom tools. It supports a 20-billion-parameter model that offers tool use capabilities, efficient inference, and compatibility with the OpenAI SDK and agentic workflows. A standout feature is its ability to cut token costs by up to 40% in Elastic 9.4—not by truncating context, but by having agents manage their own context windows intelligently. The platform also includes a multi-agent onboarding system, demonstrated in a hackathon running from January 22 to February 27, 2026, which challenges builders to create multi-step AI agents that coordinate across different data sources and actions.
In the observability platform market, Elastic competes with established players like Splunk (Cisco), Dynatrace, Datadog, Amazon Web Services (AWS), Grafana Labs, New Relic, Microsoft, and Google. While these competitors offer AI-assisted features, Agent Builder differentiates itself by being deeply embedded within Elasticsearch, providing a unified experience for data ingestion, search, and agent creation. However, Elastic's open-source licensing model has drawn criticism—users on forums like Reddit have noted that both the ELK stack and Graylog restrict alerting features to paid tiers, which may limit the tool's appeal for self-hosted or budget-conscious teams.
Despite its strengths, Agent Builder has notable trade-offs. It is tightly coupled with Elasticsearch, meaning teams using other data stores cannot leverage it without migrating. The platform's reliance on a 20B parameter model may not match the raw reasoning power of larger frontier models from OpenAI or Google. Additionally, while Elastic claims context engineering reduces token costs, the actual savings depend heavily on the complexity of agent workflows and data volume. Finally, the tool's pricing is not publicly disclosed, which can make cost estimation difficult for enterprises evaluating it against competitors with transparent pricing tiers.
How it works
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Tool use capabilities
The 20B parameter model can invoke built-in and custom tools to query Elasticsearch and execute actions.
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Efficient inference
Optimized inference reduces latency and compute overhead for real-time agent responses.
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OpenAI SDK compatible
Developers can integrate agents using the familiar OpenAI SDK, simplifying migration or hybrid setups.
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Agentic workflows
Supports multi-step, multi-agent coordination for complex tasks like onboarding or incident response.
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Native data prep and ingestion
Built-in pipelines for ingesting and preparing Elasticsearch data without external ETL tools.
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Retrieval and ranking
Uses Elasticsearch's retrieval and ranking to ground agent answers in relevant, up-to-date data.
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Context-driven AI agents
Agents manage their own context windows, cutting token costs by up to 40% in Elastic 9.4.
Strengths and trade-offs
Strengths
- Simplifies the entire agent workflow with native data prep, retrieval, and tool integration, reducing development time from weeks to minutes.
- Cuts token costs by up to 40% in Elastic 9.4 by having agents manage their own context intelligently rather than truncating data.
- Supports a 20-billion-parameter model with efficient inference, OpenAI SDK compatibility, and tool use for production-grade agents.
- Integrates deeply with Elasticsearch, allowing agents to query and act on real-time observability and search data without external dependencies.
Trade-offs
- Tightly coupled with Elasticsearch, making it unsuitable for teams using alternative data stores like PostgreSQL or Snowflake without migration.
- The 20B parameter model may underperform on complex reasoning tasks compared to larger frontier models from OpenAI or Google.
- Pricing is not publicly disclosed, creating uncertainty for enterprises that need to compare total cost against transparent competitors like Datadog or New Relic.
- Elastic's open-source licensing restricts advanced features like alerting to paid tiers, which may deter self-hosted or budget-constrained users.
Pricing context
Not publicly disclosed in the provided sources.
Getting started with Agent Builder
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Sign up for Elastic Cloud
Go to the Elastic Cloud console and create an account. Choose a deployment that includes the Agent Builder feature. Verify your email and log in to access the Elasticsearch cluster where your agents will run.
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Connect your Elasticsearch data
In the Agent Builder interface, select the data source from your Elasticsearch indices. Use the native data preparation pipeline to ingest and prepare your observability or search data. Ensure the data is indexed and accessible for agent queries.
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Configure the agent model
Select the 20-billion-parameter model provided by Agent Builder. Enable tool use capabilities and set context window management to reduce token costs. Adjust inference settings for latency or throughput based on your workload.
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Build a query agent
Define a simple agent that queries Elasticsearch for recent error logs. Add a built-in tool to retrieve data and another to generate a summary report. Test the agent by running a sample query and verifying the output.
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Deploy and schedule the agent
Deploy the agent to your production Elasticsearch environment. Set a schedule for periodic execution, such as hourly incident checks. Monitor agent performance in the dashboard and adjust context windows as needed.
Frequently Asked Questions
What is Elastic Agent Builder and what does it do?
Elastic Agent Builder is a conversational AI platform from Elastic that lets developers create context-driven agents to query and act on Elasticsearch data. It targets DevOps engineers and SREs, enabling agents to trigger alerts, generate reports, or execute workflows within the Elastic ecosystem.
How does Elastic Agent Builder cut token costs by up to 40%?
Elastic Agent Builder reduces token costs by up to 40% in Elastic 9.4 through context engineering. Instead of truncating data, agents intelligently manage their own context windows, keeping only relevant information. This lowers inference expenses without sacrificing the quality of responses for real-time tasks.
What tools and integrations does Elastic Agent Builder support?
Elastic Agent Builder includes a tool registry with built-in and custom tools for querying Elasticsearch and executing actions. It supports a 20-billion-parameter model with efficient inference, is compatible with the OpenAI SDK, and offers native data preparation, ingestion, retrieval, and ranking for agentic workflows.
How does Elastic Agent Builder compare to competitors like Datadog or Splunk?
Elastic Agent Builder differentiates by being deeply embedded within Elasticsearch, providing a unified experience for data ingestion, search, and agent creation. Competitors like Datadog and Splunk offer AI features, but Agent Builder's context engineering and native tool integration are unique. However, it requires an Elasticsearch stack.
What are the main limitations of Elastic Agent Builder?
Elastic Agent Builder is tightly coupled with Elasticsearch, so teams using other data stores cannot use it without migration. Its 20-billion-parameter model may underperform on complex reasoning versus larger frontier models. Pricing is not publicly disclosed, and advanced alerting features are restricted to paid tiers.
Is Elastic Agent Builder suitable for self-hosted or budget-constrained teams?
Elastic Agent Builder may not be ideal for self-hosted or budget-constrained teams because Elastic's open-source licensing restricts advanced features like alerting to paid tiers. Additionally, pricing is not publicly disclosed, making cost estimation difficult. The tool also requires an Elasticsearch stack, which adds migration overhead.
Alternatives
How Agent Builder compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Agent Builder
- Pricing
- Not publicly disclosed in the provided sources.
- Target
- Elastic Agent Builder is a conversational AI platform from Elastic that allows developers to create context-driven AI agents capable of querying and acting on data
- Strength
- Simplifies the entire agent workflow with native data prep, retrieval, and tool integration, reducing development time from weeks to minutes.
- Watch for
- Tightly coupled with Elasticsearch, making it unsuitable for teams using alternative data stores like PostgreSQL or Snowflake without migration.
Gumloop
- Pricing
- Free tier; Pro at $99/month; Team at $300/month; Enterprise custom.
- Target
- Operations teams building no-code AI automations for CRM, support, and data analysis.
- Deployment
- Cloud SaaS only.
- Strength
- Pre-built templates for CRM and support agent workflows.
- Watch for
- Limited enterprise governance; no on-premise deployment option.
Lyzr
- Pricing
- Free tier; Starter at $99/month; Growth at $499/month; Enterprise custom.
- Target
- Enterprises needing no-code AI agent builders with security and compliance.
- Deployment
- Cloud SaaS; on-premise available.
- Strength
- Enterprise-grade security and compliance certifications.
- Watch for
- Pricing can escalate with advanced features and usage limits.
Opsima
- Pricing
- Custom/Contact sales only.
- Target
- Industrial enterprises in ports, mines, logistics, and heavy equipment.
- Deployment
- Cloud SaaS; on-premise available.
- Strength
- Governed 5-agent architecture with industrial data backbone.
- Watch for
- Narrow focus on industrial operations; less suited for general business.
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Sources
Reporting on this tool draws on these publicly available sources.