Agentic AI transforms how cloud architects build intelligent systems on AWS. Instead of writing rigid procedural code for every workflow, you delegate goals to software agents that perceive context, reason about trade-offs, and act on your behalf. This guide covers the foundations and shows how Amazon Bedrock, AWS Lambda, and AWS Step Functions bring autonomous… Continue reading
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02 – AWS Agentic AI Patterns: From Reasoning to Multi-Agent
Agentic AI patterns are the reusable blueprints that turn a generic language model into a purposeful production system. Where Part 1 covered the why, this guide maps each pattern — reasoning, retrieval-augmented, orchestrator, multi-agent — to the AWS services that bring it to life. Agentic AI Patterns: What You’ll Learn You’ll recognize the core patterns,… Continue reading
03 – AWS Agentic AI Frameworks: Strands, LangChain, and MCP
Agentic frameworks turn a raw LLM into a dependable agent. Here we look at the frameworks, platforms, and protocols you actually install and ship on AWS: LangChain, Strands Agents, CrewAI, AutoGen, MCP, and A2A. Agentic Frameworks: What You’ll Learn Agentic frameworks encode the critical plumbing — prompt assembly, tool calling, retry loops, memory, tracing —… Continue reading
04 – AWS Multi-Tenant Agentic AI: Isolation and Cost Architecture
Multi-tenant agents turn one agentic AI system into a SaaS serving many customers from shared infrastructure. Siloed, pooled, or hybrid deployment determines your unit economics and security. Multi-Tenant Agents: What You’ll Learn This guide maps the AWS-recommended deployment models, tenant-context propagation patterns, and isolation primitives across siloed, pooled, and hybrid topologies on Bedrock AgentCore. By… Continue reading
06 – AWS RAG Optimization: Writing for Retrieval Accuracy
RAG optimization begins before a user submits a query — at the source documents that feed retrieval-augmented generation. Document and context engineering is the discipline of writing for two readers: the human who skims the page and the embedding model that chunks it. RAG Optimization: What You’ll Learn RAG optimization is the practice of writing… Continue reading
07 – AWS Vector Database for RAG: OpenSearch to pgvector
Vector database selection on AWS for RAG: compare OpenSearch, pgvector, MemoryDB, Neptune Analytics, DocumentDB, S3 Vectors, Bedrock Knowledge Bases, and Kendra in 2026.
Continue reading08 – AWS Healthcare RAG: Clinical Accuracy Architecture
Healthcare RAG on AWS using Amazon Bedrock, OpenSearch, and Neptune. Patient data augmentation, re-admission prediction, and talent management solutions grounded in HIPAA-eligible infrastructure.
Continue reading09 – AWS AgentOps: Operationalizing Agentic AI
AgentOps on AWS — operationalize agentic AI at enterprise scale with CI/CD pipelines, guardrails, observability, multi-tenancy, and business alignment strategies across the six AWS focus areas.
Continue reading10 – AWS Agentic AI Economics: Measuring ROI
Agentic AI economics compares total human costs against agent costs, introduces pay-per-outcome pricing, and gives you the ROI models to scale automation sustainably on AWS — answering when replacing human labor with agentic AI actually pays off, and where agentic AI economics tips in favor of automating. Agentic AI Economics: What You’ll Learn Agentic AI… Continue reading