DR. Roman Ceresnak

FEATURED SERIES

Production AWS AI Architectures

From RAG to Memory Systems: Building Stateful AI Architecture on AWS

RAG gives you retrieval. Memory systems give you continuity. Production memory system with 5 typed memory stores, promotion gates, hybrid retrieval on Aurora pgvector + OpenSearch, GDPR erasure, and full Terraform deployment.

✓ 5 memory types (Policy, Preference, Fact, Episodic, Trace)

✓ Hybrid retrieval (Aurora pgvector + OpenSearch)

✓ GDPR-compliant erasure

I Replaced My Custom Agent Orchestration with AWS Strands SDK — Here's What Happened

422 lines of custom ReAct loop deleted. Model-driven loop with 9 DevOps tools, HITL via Step Functions, DynamoDB session memory, and full CloudWatch observability — all on 6 Terraform modules.

✓ @tool auto schema generation

✓ waitForTaskToken HITL pattern

✓ 6 Terraform modules

PageIndex vs Classic RAG: Why Vector Search Fails on Structured Documents

Classic RAG chunks destroy legal document hierarchy. PageIndex builds a tree and lets Bedrock navigate it — achieving 100% cross-reference accuracy with zero embedding API calls at query time.

✓ Zero embedding calls at query time

✓ 100% cross-reference accuracy

✓ Section-level citations

Build Self-Learning Agents With No Fine-Tuning on AWS

Production-grade self-learning agent using Bedrock, Lambda, DynamoDB, and Step Functions. Runtime feedback loop writes new knowledge back to Bedrock Knowledge Base after every interaction.

✓ Self-learning runtime

✓ Zero GPU training

✓ Bedrock Knowledge Base

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Hey, I'm Roman

Production AWS AI Architectures

From RAG to Memory Systems: Building Stateful AI Architecture on AWS

I Replaced My Custom Agent Orchestration with AWS Strands SDK — Here's What Happened

PageIndex vs Classic RAG: Why Vector Search Fails on Structured Documents

Build Self-Learning Agents With No Fine-Tuning on AWS