Exploring the frontier of autonomous AI agents — multi-agent systems, RAG pipelines, and agentic frameworks like Strands.
13 articles in this seriesMem0, Letta, Zep, graph-RAG, Neptune Memory, HiveMemory, Obsidian steering files -- the agent memory space is fragmenting faster than it's converging. Here's a landscape analysis of why no single solution wins, the four types of memory agents actually need, and a decision framework for choosing your architecture.
AWS released the Agent Toolkit for AWS on May 6, 2026 -- a managed MCP server exposing the full AWS API surface to autonomous agents. I shipped an infrastructure agent the same week. Here's the two-phase safety pattern that lets you hand an agent the keys to your account without waking up to a $10K bill.
Your data lives in AWS, Databricks, and Microsoft Fabric. Your business glossary is in Collibra. Users just want to find data and get access. Here is an agentic architecture that makes governance the default instead of the blocker.
Your RPA estate has 50 bots. Some should become AI agents, some should stay as bots, some need a hybrid pattern. Here is a repeatable, weighted scoring rubric — and the 5 migration patterns it maps to.
Boulder uses 9 Strands agents on Bedrock AgentCore to generate, deploy, and maintain full-stack apps on AWS Amplify — with self-healing builds and self-improving prompts.
Most RAG tutorials stop at 'put vectors in a database.' This post covers what actually determines quality: how you chunk documents, which vector search engine to pick, and how to measure and iterate on retrieval performance using Bedrock Knowledge Bases and LLM-as-judge evaluation.
Vector search, semantic search, keyword search, hybrid search — these terms get used interchangeably but they mean different things. This post breaks down what each actually does, when each matters, and why hybrid search wins for RAG.
A deep dive into the multi-agent architecture behind AWS Security Agent's automated penetration testing — from specialized agent swarms to assertion-based validation.
A 5-component framework for writing effective system prompts for any AI agent — Bedrock Agents, Claude Code, LangChain, Strands, or custom builds. With a practical Claude Code implementation.
A beginner-friendly walkthrough of how an LLM actually works end-to-end: from typing a prompt to receiving a response — covering tokenization, embeddings, Transformer layers, KV cache, the training loop, embeddings for search, and why decoder-only models won.
A practical walkthrough of two paths to working with Mistral — the managed API for fast prototyping and self-hosted deployment for full control — with real code covering prompting, model selection, function calling, RAG, and INT8 quantization.
AWS now offers 9 different ways to store and search vectors for RAG workloads. This guide compares every option through the Well-Architected Framework to help you pick the right one.
Enterprise workflows often require interacting with web applications that lack APIs. Traditional automation scripts are brittle and break when UIs change.
Deep dives into artificial intelligence, large language models, transformers, embeddings, and the core concepts powering modern AI systems.
25 articlesBest practices and deep dives into cloud security — from zero trust architectures to WAF configurations and penetration testing.
18 articlesPatterns, services, and architectural decisions for building on AWS — including Bedrock, SageMaker, Transfer Family, and more.
49 articles