Elastic announced Agent Builder, a complete set of capabilities powered by Elasticsearch, that makes it easy for developers to build custom AI agents on company data—all within minutes.
Agent Builder also provides an out-of-the-box conversational experience for exploring, analyzing, and optimizing any data in Elasticsearch.
As AI agents evolve to take on more complex and data-driven enterprise tasks, reliability and accuracy depend on delivering accurate context. In most enterprises, this context is scattered across various unstructured data sources, including documents, emails, business apps, and customer feedback. The process for getting the relevant context into agents at the right time is known as context engineering. While Elasticsearch has always been a leading platform for the core of context engineering, Agent Builder dramatically expands on this strength. It simplifies the entire operational lifecycle of agents, their development, configuration, execution, customization, and observability directly into Elasticsearch.
”AI agents don’t just need lots of data, they need the right data and tools, with relevance, guardrails, and observability built in,” said Ken Exner, chief product officer at Elastic. “Developers already rely on Elasticsearch to find the right answer from their messy business data. Agent Builder goes further by making Elasticsearch one of the fastest platforms to build precise AI agents that use your data, where retrieval, governance, and orchestration all operate in one place, natively.”
With Agent Builder, developers have built-in tools that go beyond basic run queries of open-standard Model Context Protocol (MCP) endpoints. Users of Agent Builder on Elasticsearch can ask natural language questions, identify which indexes to query, configure searches, define agent parameters and more.
With Agent Builder, developers can:
- Immediately Chat with Company Data: Agent Builder includes a built-in, native conversational agent. Right out of the box, you can ask questions and interact with any data you have in Elasticsearch, turning your data into an active, conversational partner.
- Leverage Intelligent Built-In Tools for Relevance: Agent Builder comes with a set of built-in tools, including a powerful search capability that selects the right index, understands the structure of that data, translates natural language into optimized semantic, hybrid or structured queries, and returns only the most relevant context to the Large Language Model (LLM).
- Build Powerful Custom Tools: Define tools that give the agent new skills, harnessing the full power of Elasticsearch’s query language (ES|QL) to precisely control what data is used for context. This gives granular control over the relevance, accuracy, and security of your agent's responses.
- Define Custom Agents: Create your own custom agent from the ground up, going beyond the built-in options. You control the agent’s entire persona with a custom system prompt, decide exactly which tools it can access, and configure its specific security profile to meet your needs.
- Integrate with MCP and A2A Safely: Connect external agents and applications via MCP and A2A, while maintaining governance through the Elasticsearch execution layer.
Agent Builder in Elasticsearch is available today in Technical Preview on Elastic Cloud in serverless and coming soon in version 9.2.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...