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Elastic Announces General Availability of Agent Builder with Expanded Capabilities

Elastic Agent Builder grounds AI agents in enterprise data, executes context-driven answers and actions

Elastic announced the general availability of Agent Builder, a complete set of capabilities that helps developers quickly build secure, reliable, context-driven AI agents.

AI agents need the right context to perform complex tasks accurately. Built on Elasticsearch, Agent Builder excels at context engineering by delivering relevance in a unified platform that scales, searches, and analyzes enterprise data. It dramatically simplifies the entire agent workflow with native data prep and ingestion, retrieval and ranking, built-in and custom tools, native conversational experience, and agent observability. Developers can use Agent Builder to chat with their data or build a context-driven custom agent in minutes.

"Agent Builder has native MCP and A2A protocol support, enabling seamless deployments within Microsoft Foundry and Microsoft Agent Framework,” said Amanda Silver, CVP, Microsoft CoreAI. “This gives our users a way to build context-rich, agentic AI leveraging Elasticsearch as a Knowledge Source and powered by Microsoft Foundry."

"Agentic systems fail today because connecting AI to tools and data is complex," said Sam Partee, co-founder at Arcade.dev. "Elastic Agent Builder with Arcade.dev gives developers a structured, secure way to handle how agents retrieve context, reason, and act, taking agents from demo to production grade."

“Unlocking enterprise context from unstructured data sources is key to building effective agents,” said Jerry Liu, CEO at LlamaIndex. “Elastic Agent Builder combined with LlamaIndex’s complex document processing strengthens the critical context layer, helping teams retrieve, process, and prepare data so agents can reason more accurately and deliver better outcomes.”

Introducing Workflows

Elastic also introduced Elastic Workflows (tech preview), a new capability that extends Agent Builder’s functionality by enabling agents to reliably take action across systems.

Many agent-building frameworks require LLMs to plan and manage every step of the automation. However, AI lacks the reliability of rule-based actions, a critical capability for organizations. Workflows closes this gap. Now, agents built with Agent Builder can leverage Workflows to orchestrate internal and external systems to take actions, gather and transform data and context with precision. Agent Builder and Workflows enable developers to build context-driven agents that can reason accurately and execute predictably.

"Agent Builder simplifies working with messy enterprise data, giving developers a secure, reliable foundation to build context-driven agents at scale,” said Ken Exner, chief product officer at Elastic. “Elastic Workflows complements this foundation by giving those agents built-in, rules-based automation for simple tasks. By enhancing Agent Builder with Workflows, teams get a single system that delivers both intelligent reasoning and dependable automation, which is exactly what enterprises need to move from pilots to real-world impact.”

Agents developed with Agent Builder are model-agnostic and compatible with managed model-as-a-service providers, including the cloud hyperscalers.

Availability

Agent Builder is available in Elastic Cloud Serverless and is included with the Enterprise Tier in Elastic Cloud Hosted and self-managed Elastic Stack releases for existing customers.

Workflows is available in tech preview.

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Elastic Announces General Availability of Agent Builder with Expanded Capabilities

Elastic Agent Builder grounds AI agents in enterprise data, executes context-driven answers and actions

Elastic announced the general availability of Agent Builder, a complete set of capabilities that helps developers quickly build secure, reliable, context-driven AI agents.

AI agents need the right context to perform complex tasks accurately. Built on Elasticsearch, Agent Builder excels at context engineering by delivering relevance in a unified platform that scales, searches, and analyzes enterprise data. It dramatically simplifies the entire agent workflow with native data prep and ingestion, retrieval and ranking, built-in and custom tools, native conversational experience, and agent observability. Developers can use Agent Builder to chat with their data or build a context-driven custom agent in minutes.

"Agent Builder has native MCP and A2A protocol support, enabling seamless deployments within Microsoft Foundry and Microsoft Agent Framework,” said Amanda Silver, CVP, Microsoft CoreAI. “This gives our users a way to build context-rich, agentic AI leveraging Elasticsearch as a Knowledge Source and powered by Microsoft Foundry."

"Agentic systems fail today because connecting AI to tools and data is complex," said Sam Partee, co-founder at Arcade.dev. "Elastic Agent Builder with Arcade.dev gives developers a structured, secure way to handle how agents retrieve context, reason, and act, taking agents from demo to production grade."

“Unlocking enterprise context from unstructured data sources is key to building effective agents,” said Jerry Liu, CEO at LlamaIndex. “Elastic Agent Builder combined with LlamaIndex’s complex document processing strengthens the critical context layer, helping teams retrieve, process, and prepare data so agents can reason more accurately and deliver better outcomes.”

Introducing Workflows

Elastic also introduced Elastic Workflows (tech preview), a new capability that extends Agent Builder’s functionality by enabling agents to reliably take action across systems.

Many agent-building frameworks require LLMs to plan and manage every step of the automation. However, AI lacks the reliability of rule-based actions, a critical capability for organizations. Workflows closes this gap. Now, agents built with Agent Builder can leverage Workflows to orchestrate internal and external systems to take actions, gather and transform data and context with precision. Agent Builder and Workflows enable developers to build context-driven agents that can reason accurately and execute predictably.

"Agent Builder simplifies working with messy enterprise data, giving developers a secure, reliable foundation to build context-driven agents at scale,” said Ken Exner, chief product officer at Elastic. “Elastic Workflows complements this foundation by giving those agents built-in, rules-based automation for simple tasks. By enhancing Agent Builder with Workflows, teams get a single system that delivers both intelligent reasoning and dependable automation, which is exactly what enterprises need to move from pilots to real-world impact.”

Agents developed with Agent Builder are model-agnostic and compatible with managed model-as-a-service providers, including the cloud hyperscalers.

Availability

Agent Builder is available in Elastic Cloud Serverless and is included with the Enterprise Tier in Elastic Cloud Hosted and self-managed Elastic Stack releases for existing customers.

Workflows is available in tech preview.

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 ...