Bedrock

Managing AWS costs becomes increasingly complex as infrastructure grows. Organizations often struggle with cloud cost management, spending valuable engineering time manually analyzing Cost Explorer data, identifying optimization opportunities, and implementing changes. Even with dedicated cost management tools, the analysis and remediation process remains largely manual, requiring specialized expertise to interpret cost data and translate it into actionable steps. This post demonstrates how to build an automated agent that analyzes AWS costs and generates actionable recommendations to reduce cloud spend. By combining AWS Bedrock’s analytical capabilities with Cost Explorer data, the system identifies cost outliers and provides specific optimization steps that go beyond basic visualizations to deliver meaningful insights. ...

Code reviews are essential for maintaining code quality, but they can be time-consuming and often repetitive. Developers find themselves commenting on the same issues across multiple pull requests: missing tests, inconsistent naming, inadequate error handling, and numerous other routine concerns. This creates a bottleneck in the development process, as team members wait for their code to be reviewed while reviewers struggle to balance thorough reviews with their own development work. ...

“Where can I find our vacation policy?” “What’s the process for requesting new hardware?” “Can you explain our security guidelines?” These questions echo through company Slack channels daily, interrupting workflows and creating redundant work for team leads and HR staff. The same questions get asked repeatedly, and answers are buried in documentation that’s difficult to navigate. In this post, I’ll show you how to build a simple yet powerful Q&A bot for Slack that leverages your company’s documentation to provide accurate, contextual answers. The best part? It runs entirely on AWS managed services, minimizing operational overhead while delivering immediate value to your organization. ...

The Real Problem: Production, Not Prototypes Everyone can demo generative AI. Almost no one can run it safely in production. Enterprises in finance, healthcare, and the public sector aren’t blocked by technology capabilities—they’re blocked by governance requirements that today’s AI implementations rarely satisfy. These organizations face three critical blockers: Data leakage risk: Sensitive information, from PII to trade secrets, flowing through public model APIs Lack of auditability: No reliable record of prompts, responses, or who accessed what information Unclear ownership: Ambiguous rights over prompt engineering IP, training data, and generated outputs AWS customers don’t want AI that behaves like a chatbot toy. They need AI that behaves like enterprise infrastructure: secured, monitored, audited, governed, and compliant with their existing security posture. ...

What is the Model Context Protocol? The Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems. Think of it as a universal adapter that lets any AI agent talk to any tool or data source without custom integration code. Anthropic announced MCP in November 2024 and donated it to the Linux Foundation’s Agentic AI Foundation a month later. The adoption has been swift: OpenAI integrated it into ChatGPT, Google DeepMind uses it for Gemini agents, AWS built AgentCore around it, and development tools like Zed, Sourcegraph, Replit, and Codeium all support it. In just a few months, the community has built thousands of MCP servers. The protocol has become the de-facto standard for agent-to-tool communication. ...