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
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...