Series · 8 posts, in reading order
Building agents with Strands and Groq: tools, memory, multi-agent patterns and the protocols agents talk through.
·18 min read·AI & LLMs
Agentic AI · Part 1
What an agent really is, how the Strands SDK and Groq fit together, and a step-by-step first agent with a tool, a prebuilt calculator and a system prompt.
·18 min read·AI & LLMs
Agentic AI · Part 2
How a Python function becomes a tool an agent can call, built up with a tip calculator, a three-tool sales assistant and an inventory checker, plus the lessons each one teaches.
·45 min read·AI & LLMs
Agentic AI · Part 3
A first-timer's guide to agent memory: long-term facts with Mem0 and FAISS, short-term conversation state with LangGraph checkpointing, and every block of code explained.
·23 min read·AI & LLMs
Agentic AI · Part 4
A first-timer's guide to multi-agent patterns with Strands and Groq: a supervisor with workers, an agent used as a tool, a graph with explicit routing, and a swarm that hands off work, with the lessons each one teaches.
·40 min read·AI & LLMs
Agentic AI · Part 5
A first-timer's guide to the two protocols agents talk through: MCP to reach tools, data and prompts, and A2A to reach other agents. It builds up from a protocol-only demo to Strands agents and a three-agent travel planner, and reads a Groq rate-limit error line by line on the way.
·55 min read·AI & LLMs
Agentic AI · Part 6
Moving the agents from this series off my laptop and onto AWS in five labs: AgentCore Runtime, Lambda, ECS on Fargate, AgentCore Gateway, and the MCP and A2A system from the last post on AgentCore. It covers how to choose where an agent should live, and every error I hit on Windows on the way.
·41 min read·AI & LLMs
Agentic AI · Part 7
The course section after deployment: tracing a Strands agent with OpenTelemetry and reading the traces in the console, Jaeger, Langfuse, LangSmith and CloudWatch; scoring an agent with AgentCore Evaluations; and limiting it with Bedrock Guardrails and Cedar policies. It includes a step-by-step guide to finding the traces I couldn't see in CloudWatch and LangSmith.
·83 min read·AI & LLMs
Agentic AI · Part 8
One trip-planning agent grown lesson by lesson, from a SQLite table to RAG, tools, an MCP server, long-term memory and a second agent over A2A. It covers every command, error and fix from my run: uv off the PATH, an OpenAI 429 that wasn't a rate limit, a Gemini 400 caused by dropped thought signatures, and an A2A timeout that looked like an outage. There's a reproduce-it-yourself guide at the end.