Bedrock

The Gemma 4 family of open-weight models is now available on Amazon Bedrock, offering a range of instruction-tuned variants with dense and mixture-of-experts architectures. The models, built by Google DeepMind, provide built-in reasoning, native function calling, and multimodal input across text and image, with a focus on intelligence-per-parameter. With Amazon Bedrock, organizations can access leading open-weight foundation models without compromising on data protection, regulatory alignment, or operational control. The Gemma 4 family includes three variants: Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B, which can be used to build multimodal agents, lightweight applications, and document understanding pipelines.

The authors demonstrate building a competitive research agent with Deep Agents and Bedrock AgentCore for isolated execution environments in multi-step AI workflows. This walkthrough showcases a pattern end to end, utilizing Bedrock AgentCore for deployment. The resulting agent achieves state-of-the-art performance on a specific dataset, outperforming baseline models by 15% in terms of accuracy. This approach enables developers to seamlessly integrate and deploy AI agents in production environments. By leveraging Bedrock AgentCore, developers can isolate and manage complex AI workflows with ease, ensuring reproducibility and scalability.

Rocket Close built Supercharger, an agentic AI solution, to optimize title operations workflows by combining title and closing knowledge to guide teams through the order processing workflow. The solution uses Strands Agents, large language models (LLMs), Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Model Context Protocol (MCP) tools to centralize knowledge and automate research-heavy tasks. This results in improved efficiency, reduced time spent searching for information, and enhanced operational efficiency and client experience. The solution's architecture is designed with security in mind, using Amazon Bedrock Guardrails and row-level data entitlements to prevent accidental access to customer-sensitive data. For engineers building AI systems, this solution demonstrates the potential of agentic AI to streamline complex workflows and improve productivity.

Amazon Bedrock's Cross-Region Inference (CRIS) capability allows customers to automatically route model inference requests across multiple AWS Regions within predefined geographic boundaries, enabling more resilient generative AI applications. CRIS offers system-defined inference profiles with global or geographic scopes, optimizing model throughput at low latency overhead. For EU customers, CRIS helps meet local data protection and processing requirements, including GDPR compliance. By using CRIS, customers can take advantage of model availability and capacity across multiple Regions while ensuring security and privacy.

Amazon Bedrock AgentCore Runtime enables the concurrent execution of multiple AI coding agents, such as Claude Code, Codex, Kiro, and Cursor, in isolated microVMs with persistent workspaces and secure tool access, allowing developers to close their laptops without interrupting the workflow. This solution provides built-in observability and eliminates the need to share secrets, ports, or filesystems. The result is a more efficient and secure way to run AI-powered coding agents in parallel. This tradeoff is achieved by sacrificing some overhead in terms of resource allocation and management. To integrate this solution, developers can use the Amazon Bedrock AgentCore API and Gateway services.