AWS, AI/ML, and cloud architecture insights
Your Bedrock Knowledge Base returns great chunks — but your answers are still wrong. The problem isn't retrieval. It's the hidden layer between retrieval and generation: chunk ordering, silent truncation, and metadata filters that run before search.
Most AI agents suffer from goldfish memory — every conversation starts from zero. Here's how to build a 3-layer memory system for Bedrock Agents using SESSION_SUMMARY and Aurora pgvector, with a live cross-session retrieval demo in 310ms.
I had 422 lines of custom ReAct loop — hand-written tool schemas, an XML parser that broke when the model was creative, and a dispatcher that grew with every new capability. Here's what happened when I replaced it all with AWS Strands SDK.
Classic RAG chunks destroy document hierarchy — cross-references between legal sections become unanswerable. PageIndex builds a tree (Document→Article→Section→Clause), lets Bedrock navigate it directly, and achieves 100% cross-reference accuracy with zero embedding API calls at query time.
Production-grade self-learning agent on AWS using Amazon Bedrock, Lambda, DynamoDB, and Step Functions. Runtime feedback loop writes new knowledge back to Bedrock Knowledge Base after every interaction — zero GPU training required.
Benchmark of three knowledge retrieval patterns — RAG with FAISS, CAG with Bedrock prompt caching, and KAG with Amazon Neptune — showing up to 50% token cost reduction with full Terraform infrastructure.
Agentic systems have emergent costs — every reasoning step adds tokens, every tool call adds latency and Gateway ops, every memory event adds a line to your bill. Here's how to architect 100 agents without a surprise invoice at month-end.
Production blueprint for building enterprise AI agents that reason, plan, and execute autonomously. Complete architecture using AWS Bedrock, EventBridge, DynamoDB, and CDK with Pydantic validation and self-healing capabilities.
Fully automated system using AWS Bedrock with Claude Sonnet 4.5 to analyze every Terraform plan before deployment. Detect security risks and cost anomalies for less than $6 per month.
AI-powered security automation that analyzes Security Hub findings and generates Terraform remediation code automatically. From detection to fix in 30 seconds — for $1.50/month.
Intelligent $2/month serverless cost monitoring system that combines Z-score statistics with Claude 3.5 Sonnet via Bedrock to provide actionable insights on AWS bill spikes.
A production-ready browser game with real-time leaderboards, anti-cheat detection, and global CDN distribution — all built on AWS serverless for pennies per player.
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Finding PII in code, checking CVEs, and analyzing ML datasets requires completely different approaches. Here's how I built 7 specialized Lambda functions that work together like a well-oiled machine.
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A serverless system that scans code, configs, and docs against GDPR and EU AI Act requirements—and generates compliance reports in under 2 minutes.
A comprehensive production-ready solution combining Infrastructure as Code (Terraform), automated CI/CD pipelines (GitHub Actions), and best practices for deploying guardrails in real-world environments.
A production-ready multi-agent orchestration platform using 100% native AWS services. No LangChain. No LangGraph. No third-party orchestration layer.
Upload a diagram. Get a structured review across all six pillars in under 30 seconds — no AWS consultant required.
A complete serverless system for vehicle detection and traffic analysis built on AWS Lambda, DynamoDB, and Terraform — for less than $0.50/month.
Every time a developer opens a PR, an AI catches security issues, missing best practices, and cost problems — before any human reviewer sees the code.