Official technical community of Amazon Cloud. Shares practical AI/ML, big data, database, modern app development, IoT content, offers comprehensive learning resources, hosts regular developer events, and continuously empowers developers.
Amazon Bedrock’s AgentCore introduces four preview‑stage capabilities—fine‑grained Policy, continuous Evaluations, long‑term Memory, and bidirectional Runtime streaming—each illustrated with real‑world use cases that boost efficiency, cut costs, and improve operational control for large‑scale AI agents.
Amazon Bedrock now offers 18 additional fully managed open‑source models from providers such as Google, Mistral AI, NVIDIA and OpenAI, bringing the total to nearly 100 serverless models; the new offerings include Mistral Large 3 and three Ministral 3 variants optimized for edge deployment, and can be accessed via a unified API without modifying existing application code or infrastructure, while Amazon’s Guardrails and evaluation tools help ensure security and compliance.
Technical debt eats roughly 20% of IT budgets, but Amazon Transform custom—an AI‑driven code‑modernization agent—claims to slash migration task time by up to 80%, offering built‑in and custom conversion rules for Java, Node.js, Python, and infrastructure code via CLI or Web UI.
Monus AI leverages Amazon Bedrock AgentCore and a five‑level agent architecture to recognize consumer decision stages with 94% accuracy, boost data‑processing speed threefold, slash overall processing cost by 80%, and reduce response time by 60%, fundamentally reshaping e‑commerce AI search.
This article demonstrates how to build a smart to‑do application that uses Amazon Nova Sonic’s real‑time bidirectional streaming API to enable hands‑free voice interaction, detailing the underlying AI model, AWS serverless architecture, deployment steps, and verification procedures.
This article analyzes the unique security threats introduced by Agentic AI—such as memory poisoning, tool abuse, and MCP server vulnerabilities—then presents a layered mitigation framework, practical Secure SDLC recommendations, and concrete Amazon Bedrock Guardrails code examples to help engineers build trustworthy, resilient Agentic AI systems.
The guide outlines a comprehensive, multi‑dimensional observability framework for AI agents—covering behavior insight, quality assessment, latency and token metrics, tool‑call tracking, error tracing, and cost monitoring—while demonstrating practical implementation with OpenTelemetry, Amazon CloudWatch, and open‑source tools such as MLflow and Langfuse.
This article explains why evaluating AI agents is essential, outlines a multi‑dimensional metric system covering performance, safety, cost and bias, describes common evaluation frameworks such as AgentBoard, AgentBench and τ‑bench, and provides step‑by‑step instructions, example datasets and code for building a robust agent assessment pipeline.
This guide analyzes recent Agentic AI security incidents, explains core identity concepts, compares 2‑leg and 3‑leg OAuth flows, highlights the confused‑deputy problem, and presents Amazon Bedrock AgentCore Identity solutions with concrete code and deployment steps to build a trustworthy end‑to‑end authentication and authorization system.
This guide explains the Model Context Protocol (MCP) architecture, compares local and remote deployment modes, and provides step‑by‑step instructions for hosting MCP servers on Amazon Bedrock, Lambda, and ECS/Fargate, highlighting performance, security, and operational trade‑offs.
