Ai-agents

How to choose among eight RAG architecture patterns, from Simple RAG to Agentic RAG, by matching each pattern to the specific failure mode it solves.

Prompt changes are code changes — they should be tested the same way. This post covers the mechanics of A/B testing prompts against live traffic, the metrics worth tracking, and the tooling that makes it tractable.

AWS has published prescriptive guidance on agentic AI patterns — from orchestrator-worker hierarchies to event-driven choreography. This post maps those patterns to real infrastructure decisions on Lambda, ECS, and Bedrock.

LangGraph models agents as explicit graphs with typed state, built-in checkpointing, and human-in-the-loop support. Here is what that means in practice for building agents that actually run reliably in production.

LangGraph gives you explicit control over agent state, branching, and cycles — the things that make the difference between a demo and a production AI system. Here is how to think about it and where to start.