Category: Artificial Intelligence

Agentic AI systems are rapidly expanding beyond the digital world and into the physical, where AI agents perceive, reason, and act in real environments. As AI systems increasingly interact with the physical world through robotics, autonomous vehicles, and smart infrastructure, a fundamental question emerges: how do we build agents that leverage massive cloud compute for […]

AWS is excited to continue our support for Model Context Protocol (MCP) as it moves under the Linux Foundation.  This move enables us, our partners, and our customers to be more confident in the long-term success of the protocol which has become a standard  component of agentic architectures. By open sourcing MCP in 2024, Anthropic […]

Jupyter notebooks have become a popular tool for data scientists, researchers, educators and analysts who need to experiment with code, visualize data, and document their findings. Many users run Jupyter on their laptops. This creates limitations to collaborate with a distributed team because users cannot securely provide direct access to their local JupyterLab application over […]

Today we are introducing CLI Agent Orchestrator (CAO, pronounced “kay-oh”), an open source, multi-agent orchestration framework that transforms how developers work with AI-powered CLI tools such as Amazon Q CLI and Claude Code. While individual developer CLI tools excel at focused tasks with sophisticated reasoning and autonomous execution, complex enterprise development projects often require coordination […]

Until recently, building AI agents meant wrestling with complex orchestration frameworks. Developers wrote elaborate state machines, predefined workflows, and extensive error-handling code to guide language models through multi-step tasks. We needed to build elaborate decision trees to handle “what if the API call fails?” or “what if the user asks something unexpected?” Despite this effort, […]

Welcome to Part 4 of our blog series on Open Protocols for Agent Interoperability where we will cover the Agent-to-Agent (A2A) protocol, AWS’ involvement with the Linux Foundation-based open standard, and our support of A2A in the Strands Agents SDK. Here is what we’ve covered so far: Part 1: How the Model Context Protocol (MCP) […]

Today we are excited to announce version 1.0 of the Strands Agents SDK, marking a significant milestone in our journey to make building AI agents simple, reliable, and production-ready. Strands Agents is an open source SDK that takes a model-driven approach to building and running AI agents in just a few lines of code. Strands […]

Developers are architecting and building systems of AI agents that work together to autonomously accomplish users’ tasks. In Part 1 of our blog series on Open Protocols for Agent Interoperability we covered how Model Context Protocol (MCP) can be used to facilitate inter-agent communication and the MCP specification enhancements AWS is working on to enable […]

In Part 1 of our blog series on Open Protocols for Agent Interoperability we covered how the Model Context Protocol (MCP) can be used to facilitate inter-agent communication and the MCP specification enhancements AWS is working on to enable that. In Part 2 of this blog series we dive deep into authentication in the latest […]

When we introduced the Strands Agents SDK, our goal was to make agentic development simple and flexible by embracing a model-driven approach. Today, we’re excited to highlight how you can use Claude 4’s interleaved thinking beta feature with Strands to further simplify how you write AI agents to solve complex tasks with tools. With a […]