OpenAI Agents on AWS: Simplifying State Management

AWS just launched an AWS Serverless plugin for Claude Code. It ships the following things: - Skills: Lambda, Deployment, Durable Functions - MCPs: Serverless MCP - Hooks: Validate SAM Templates All of these give your AI assistant more specialised context. The art is in giving the CORRECT context at the correct times. I'll give it a try in my day-to-day work and let you know how it behaves. Thanks for supporting other CLIs than just Kiro 😊 🔗 https://lnkd.in/dbjEuQ-T

Cerebras is coming to AWS. Amazon Web Services (AWS) and Cerebras are bringing ultra-fast inference to Amazon Bedrock. 🚀⚡ This is a first-of-its-kind deployment: AWS and Cerebras are co-developing a disaggregated inference architecture combining AWS Trainium 3 and Cerebras WSE-3. Trainium handles prefill. Cerebras handles decode. Purpose-built silicon for each phase of inference. Disaggregating the workload increases throughput by up to 5x, helping meet the massive demand for fast tokens driven by agentic coding and next-generation AI applications. The result: customers already building on AWS will soon have the speed of Cerebras at their fingertips. Read more: https://lnkd.in/eSpwRXu2

Amazon Web Services (AWS) and Cerebras are partnering to deliver the fastest AI inference available through Amazon Bedrock Cerebras is the world's fastest AI inference system. It delivers thousands of times greater memory bandwidth than the fastest GPU. As reasoning models now represent a majority of inference to compute and generate more tokens per request as they “think” through problems, the need to accelerate this portion of the workflow has grown accordingly. OpenAI, Cognition, Mistral, and others use Cerebras to accelerate their most demanding workloads, especially agentic coding where developer productivity is constrained by inference speed. Reach out to learn more: 💥🚀

Inference infrastructure is starting to reflect a simple reality: different parts of the workload stress hardware in very different ways. When you allocate work to the systems that do it best, performance jumps - for inference prefill-decode up to 5x!! Excited about AWS + Cerebras collaboration and exploring the direction through disaggregated inference architectures. 🚀

Today Cerebras announced that Amazon Web Services (AWS) will be deploying Cerebras CS-3s in their data centers. Together, Cerebras and AWS will be delivering the fastest inference solution in the world. Today we announce a multi-year partnership with the industry’s largest cloud provider, AWS to deliver even faster inference solutions through disaggregation. Together we will be building fast inference solutions comprised of Trainium 3 doing prefill and Cerebras’ Wafer Scale Engine doing decode. Cerebras is now powering the world's #1 AI provider, OpenAI. And will be available in the worlds #1 cloud, AWS. Cerebras #iamcerebras

Excited to see this announcement from Cerebras. AWS will be deploying Cerebras CS-3 systems in its data centers, expanding access to the worlds fastest AI inference through one of the world’s largest cloud platforms. Proud to support the Cerebras team and the work they’re doing to push the boundaries of AI infrastructure. #ArtificialIntelligence #AIInfrastructure #AWS #Inference #Cerebras #GenerativeAI

Big Win for Cerebras and developers who wants ultra-fast Inference!!! We’re excited to announce our collaboration with Amazon Web Services (AWS) to bring the world’s fastest inference to the world’s biggest cloud. Together, we’re bringing Cerebras to Amazon Bedrock - combining AWS infrastructure and Trainium with the Cerebras Wafer-Scale Engine to push the frontier of AI inference.

Big AWS announcement 🚀 Amazon Web Services (AWS) and Cerebras are teaming up to build the fastest possible inference. Coming soon to Amazon Bedrock, this new architecture will deliver inference performance an order of magnitude faster than what’s available today by connecting AWS Trainium3 for compute-intensive prefill with Cerebras CS-3 to power decode. In simple terms: 🧠 Prefill = when the model processes and understands the full prompt/context ⚡ Decode = when the model generates the response token by token What is interesting here is the architecture: AWS is optimizing each phase separately instead of treating inference as one single step. That is a big deal for latency-sensitive GenAI workloads like coding assistants, agents, and interactive applications. Very exciting to see AWS pushing innovation not only at the model layer, but also at the inference systems layer 👏 Learn more about the partnership. https://go.aws/4utMFSX #AWS #AmazonBedrock #GenerativeAI #Trainium3 #Cerebras #Inference #AIInfrastructure #AgenticAI

Missed our live masterclass on ML Deployment on AWS – From Notebook to Production? No worries — the recording is now available. In this session, we covered how to take a machine learning model from a notebook environment to real production using AWS services like EC2, SageMaker, and Lambda, along with a live deployment demo. If you are learning AI / ML / MLOps, this is one of the most important practical skills to understand. Watch the full replay here: https://lnkd.in/gBUQfwN3 #MLOps #AWS #DataScience #MachineLearning #Webinar #CloudComputing #agilefever #masterclass AgileFever

Last week, I went hands-on with Amazon Bedrock AgentCore using Strands Agents + Nova Pro. I got to explore what it actually takes to run production-grade AI agents with the BeSA Agentic AI on AWS program. Some things I put into practice: • Deploying agents with AgentCore Runtime for scalable, serverless execution • Enabling agents to run Python and automate web tasks using Code Interpreter and Browser tools • Securing external API access with AgentCore Identity instead of hardcoded credentials • Turning APIs into agent tools with AgentCore Gateway + MCP servers • Adding memory (the coolest part), observability, and tracing to monitor agent behavior in production Building an agent is actually the easy part...but curating an infrastructure that lets agents run securely, remember context, use tools, and scale is the real architecture challenge. 🤖☁️