AWS, AI/ML, and cloud architecture insights
Exact vs semantic retrieval, the AgentCore Memory SDK, Bedrock prompt-caching economics, full observability, real cost numbers ($0.0037/turn at 10k turns/day), multi-agent memory sharing, evaluation, and the five-gate production checklist.
The complete AWS architecture end to end, a working Terraform module that provisions Aurora pgvector, DynamoDB, OpenSearch Serverless, Kinesis, and Step Functions. Six-phase migration path from RAG to memory systems, and the anti-patterns worth checking.
The promotion gate that decides what earns a durable write, token-budgeted prompt assembly for Bedrock caching, how facts get superseded instead of deleted, and how scope is enforced down to row-level security and GDPR erasure.
The fixed six-step per-turn loop, why retrieval needs two separate paths instead of one, how vector and lexical scores get fused (0.4/0.6 weighting), and why the memory manager matters more than the vector store underneath it.
Why basic RAG falls short of stateful AI, the five types of memory (Policy, Preference, Fact, Episodic, Trace) and the AWS service behind each one, short-term vs long-term storage, and single-store vs typed-table schema design.
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.