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
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 ...