A space where I share learnings, challenges, and technical solutions around AWS and Generative AI. From cloud architectures to practical AI implementations, here you will find detailed technical content born from real-world experience. Let us learn and grow together as a community.
Every time you restart a coding agent, it loses everything: installed dependencies, generated files, project state. AgentCore Runtime Session Storage solves this with an AWS-managed persistent filesystem. Full implementation with Strands Agents and real, tested code.
How I migrated my AWS news agent from defensive parsing to Bedrock Structured Outputs, eliminated 130 lines of code, and cut API calls in half. The difference between asking the model to be consistent and guaranteeing it.
Stop bloating your system prompt with edge cases. Discover how AgentCore Episodic Memory transforms your agent's past interactions into structured episodes and reflections that improve its performance over time. Complete implementation with boto3 and a real DevOps use case.
Technical analysis of AgentCore Policy announced at AWS re:Invent 2025. Real implementation of deterministic control for AI agents with working code and traces.
Amazon Bedrock AgentCore Evaluations: LLM-as-a-Judge with 13 built-in evaluators, continuous online monitoring, and custom evaluators. Practical guide with code.
Eliminate AI hallucinations with Bedrock Automated Reasoning Checks: formal mathematical verification for LLMs. Step-by-step tutorial with real example and precision metrics.
Build a digital forensics AI assistant with MCP + Amazon Bedrock: auto-discovery of specialized tools, FastMCP server, and intelligent Bedrock client. Production code included.
Implement conversation persistence with Amazon Bedrock Session Management APIs: maintain context across sessions with LangGraph and proven design patterns.
Learn to automate product review generation using Amazon Bedrock Flows and Claude 3.5 Sonnet. Step-by-step practical guide with real examples.
Bedrock Prompt Routing automatically selects the optimal model and reduces AI costs up to 94%. Step-by-step tutorial with cost analysis from the AWS console.
Implement Amazon Bedrock Guardrails: modular content filters, PII detection and redaction, real-time streaming, and DRAFT-to-production versioning. With code.
Orchestrate specialized AI agents with Amazon Bedrock Multi-Agent: a virtual marketing team for social media content. With code and real examples.
Build an industrial AI assistant with Amazon Bedrock Agents: AI-optimized APIs, technical knowledge base, and IoT integration. Real code included.
Combine Amazon Bedrock with SQL in Athena for precise deterministic answers. A hybrid architecture that overcomes LLM limitations in enterprise data analysis.
Integrate Amazon Bedrock with Aurora MySQL for intelligent SQL queries: a generative AI assistant inside your database. Step-by-step guide with code.
Every time you restart a coding agent, it loses everything: installed dependencies, generated files, project state. AgentCore Runtime Session Storage solves this with an AWS-managed persistent filesystem. Full implementation with Strands Agents and real, tested code.
How I migrated my AWS news agent from defensive parsing to Bedrock Structured Outputs, eliminated 130 lines of code, and cut API calls in half. The difference between asking the model to be consistent and guaranteeing it.
Stop bloating your system prompt with edge cases. Discover how AgentCore Episodic Memory transforms your agent's past interactions into structured episodes and reflections that improve its performance over time. Complete implementation with boto3 and a real DevOps use case.
Technical analysis of AgentCore Policy announced at AWS re:Invent 2025. Real implementation of deterministic control for AI agents with working code and traces.
Amazon Bedrock AgentCore Evaluations: LLM-as-a-Judge with 13 built-in evaluators, continuous online monitoring, and custom evaluators. Practical guide with code.
Eliminate AI hallucinations with Bedrock Automated Reasoning Checks: formal mathematical verification for LLMs. Step-by-step tutorial with real example and precision metrics.
Knowledge has to be improved, challenged, and constantly increased, or it vanishes -Peter Drucker
Solutions Architect, AWS Golden Jacket, with a passion for sharing knowledge. As an active AWS Community Builders member, former AWS Ambassador, and AWS User Group Leader, I dedicate myself to building bridges between technology and people. A Java developer at heart and independent consultant, I take cloud architecture beyond theory through international conferences and real-world solutions. My insatiable curiosity for learning and sharing keeps me in constant evolution alongside the tech community.
Every Sunday, actionable AWS tips, real cloud architecture experiences, and the best technical GenAI and development insights land in your inbox. Practical knowledge, direct and no fluff.
