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Elastic Achieves the AWS Agentic AI Specialization

Elastic has achieved the Amazon Web Services (AWS) Agentic AI Specialization, a new category launched within the AWS AI Competency. 

This specialization recognizes Elastic as an AWS Partner that enables customers to deploy smart, self-operating AI systems that can process, plan, and work independently to execute complex business processes.

The AWS AI Specialization in Agentic AI distinguishes Elastic as an AWS Partner with proven technical expertise and customer success in delivering production-ready autonomous AI systems that reason, plan, collaborate, utilize tools, execute tasks, and continuously improve. Elastic is providing deeply embedded agentic AI solutions using Amazon Bedrock AgentCore and other AWS-compatible frameworks like Strands. This helps customers move beyond experiments and deploy autonomous systems that deliver real, measurable value.

”The AWS Agentic AI Specialization is recognition of how the Elasticsearch platform for context engineering makes it easy to build AI agents that give the right answers and take the right actions,” said Alyssa Fitzpatrick, global vice president of Partner Sales at Elastic. “We use this robust platform to build agentic experiences into our product so that, for example, our Observability and Security customers can investigate and resolve issues fast.”

To make agentic AI more effective in real-world business settings, agents need to have the right context, which comes from scoping their actions and responses to post-training data locked away in silos across a company. Elasticsearch is an open, extensible context engineering platform that stores and searches structured and unstructured data and provides the retrieval and tool-building capabilities that agents need to successfully navigate complex tasks.

Elastic recently introduced Agent Builder, a set of capabilities powered by Elasticsearch, that makes it easy for developers to quickly build custom AI agents on their data. Agent Builder allows users to compose custom agents that use sophisticated tools for querying the relevant data, enabling conversation-based data exploration and automation. Agent Builder is built on Amazon Bedrock and utilizes reasoning models from the Anthropic family by default.

Amazon Agentic AI Specialization ensures customers can confidently select partners who demonstrate validated expertise in building and implementing enterprise-grade AI agents. These specialized partners help organizations deploy autonomous AI systems that can handle end-to-end business processes across diverse use cases, including enterprise knowledge operations, intelligent process automation, autonomous customer operations, financial operations automation, and supply chain optimization.

This expansion of the AWS AI Specialization now includes partners that demonstrate advanced capabilities delivering enterprise-ready generative AI and agentic AI systems to customers.

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Elastic Achieves the AWS Agentic AI Specialization

Elastic has achieved the Amazon Web Services (AWS) Agentic AI Specialization, a new category launched within the AWS AI Competency. 

This specialization recognizes Elastic as an AWS Partner that enables customers to deploy smart, self-operating AI systems that can process, plan, and work independently to execute complex business processes.

The AWS AI Specialization in Agentic AI distinguishes Elastic as an AWS Partner with proven technical expertise and customer success in delivering production-ready autonomous AI systems that reason, plan, collaborate, utilize tools, execute tasks, and continuously improve. Elastic is providing deeply embedded agentic AI solutions using Amazon Bedrock AgentCore and other AWS-compatible frameworks like Strands. This helps customers move beyond experiments and deploy autonomous systems that deliver real, measurable value.

”The AWS Agentic AI Specialization is recognition of how the Elasticsearch platform for context engineering makes it easy to build AI agents that give the right answers and take the right actions,” said Alyssa Fitzpatrick, global vice president of Partner Sales at Elastic. “We use this robust platform to build agentic experiences into our product so that, for example, our Observability and Security customers can investigate and resolve issues fast.”

To make agentic AI more effective in real-world business settings, agents need to have the right context, which comes from scoping their actions and responses to post-training data locked away in silos across a company. Elasticsearch is an open, extensible context engineering platform that stores and searches structured and unstructured data and provides the retrieval and tool-building capabilities that agents need to successfully navigate complex tasks.

Elastic recently introduced Agent Builder, a set of capabilities powered by Elasticsearch, that makes it easy for developers to quickly build custom AI agents on their data. Agent Builder allows users to compose custom agents that use sophisticated tools for querying the relevant data, enabling conversation-based data exploration and automation. Agent Builder is built on Amazon Bedrock and utilizes reasoning models from the Anthropic family by default.

Amazon Agentic AI Specialization ensures customers can confidently select partners who demonstrate validated expertise in building and implementing enterprise-grade AI agents. These specialized partners help organizations deploy autonomous AI systems that can handle end-to-end business processes across diverse use cases, including enterprise knowledge operations, intelligent process automation, autonomous customer operations, financial operations automation, and supply chain optimization.

This expansion of the AWS AI Specialization now includes partners that demonstrate advanced capabilities delivering enterprise-ready generative AI and agentic AI systems to customers.

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...