Category: HAQM Bedrock Agents

In this post, we combine HAQM Bedrock Agents and Foursquare APIs to demonstrate how you can use a location-aware agent to bring personalized responses to your users.

In this post, we demonstrate how to orchestrate multiple HAQM Bedrock agents to create a sophisticated HAQM EKS troubleshooting system. By enabling collaboration between specialized agents—deriving insights from K8sGPT and performing actions through the ArgoCD framework—you can build a comprehensive automation that identifies, analyzes, and resolves cluster issues with minimal human intervention.

This post demonstrates how enterprises can implement a scalable agentic text-to-SQL solution using HAQM Bedrock Agents, with advanced error-handling tools and automated schema discovery to enhance database query efficiency.

In this post, we explore how AI agents can streamline compliance and fulfill regulatory requirements for financial institutions using HAQM Bedrock and CrewAI. We demonstrate how to build a multi-agent system that can automatically summarize new regulations, assess their impact on operations, and provide prescriptive technical guidance. You’ll learn how to use HAQM Bedrock Knowledge Bases and HAQM Bedrock Agents with CrewAI to create a comprehensive, automated compliance solution.

In this post, we focus specifically on enabling end-users to approve actions and provide feedback using built-in HAQM Bedrock Agents features, specifically HITL patterns for providing safe and effective agent operations. We explore the patterns available using a Human Resources (HR) agent example that helps employees requesting time off.

We’re excited to announce the open source release of AWS MCP Servers for code assistants — a suite of specialized Model Context Protocol (MCP) servers that bring HAQM Web Services (AWS) best practices directly to your development workflow. This post is the first in a series covering AWS MCP Servers. In this post, we walk through how these specialized MCP servers can dramatically reduce your development time while incorporating security controls, cost optimizations, and AWS Well-Architected best practices into your code.

Today, MCP is providing agents standard access to an expanding list of accessible tools that you can use to accomplish a variety of tasks. In this post, we show you how to build an HAQM Bedrock agent that uses MCP to access data sources to quickly build generative AI applications.

In this post, we explore how CrewAI’s open source agentic framework, combined with HAQM Bedrock, enables the creation of sophisticated multi-agent systems that can transform how businesses operate. Through practical examples and implementation details, we demonstrate how to build, deploy, and orchestrate AI agents that can tackle complex tasks with minimal human oversight.

This post presents a comprehensive AIOps solution that combines various AWS services such as HAQM Bedrock, AWS Lambda, and HAQM CloudWatch to create an AI assistant for effective incident management. This solution also uses HAQM Bedrock Knowledge Bases and HAQM Bedrock Agents. The solution uses the power of HAQM Bedrock to enable the deployment of intelligent agents capable of monitoring IT systems, analyzing logs and metrics, and invoking automated remediation processes.

AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS Systems Manager self-service automation runbooks. These runbooks are created by AWS Support Engineering with best practices learned from solving customer issues. They enable AWS customers to troubleshoot, diagnose, and remediate common issues with their AWS resources. In this post, we explore how to use the power of HAQM Bedrock Agents and AWS Support Automation Workflows to create an intelligent agent capable of troubleshooting issues with AWS resources.