AI Agents

Six agentic protocols. Sixty seconds each. Built with Manim + local TTS at zero cost. All on the DPO YouTube channel.

The default state of a language model is amnesia. Every session, it wakes up fresh with no memory of what happened before. I built a memory system that fixes this — and somewhere in the process, the agent got a name, a personality, and an opinion about font choices. The Problem # Every LLM session is stateless by design. You can inject previous conversation history, but:

Luminar has 173 source files, 21,586 lines of production code, 43 API endpoints, and 155+ tests. It was built almost entirely by AI agents. Here’s the team structure, the workflow, and the honest truth about what breaks. The Team # I didn’t want generic agents. I wanted specialists — each with a clear domain, sharp ownership boundaries, and a persona that shapes how they approach problems.

Building production AI agents requires choosing the right framework. This analysis examines pi-agent-core (OpenClaw’s runtime), Google ADK, AWS Strands, CrewAI, LangGraph, and Pydantic AI across critical dimensions: sessions, memory, protocols, agent loops, and replay support.

I’ve been obsessed with a question: Why can’t AI just… do things? ChatGPT can write a perfect email, but you still copy-paste it. Claude can explain how to automate your workflow, but you implement it. Then I found OpenClaw — and everything clicked. The Problem With Chatbots # Traditional AI: Smart brain, no body. Limited to generating text.

Executive Summary # TL;DR # As of January 2026, the agentic ecosystem is consolidating around four complementary protocols that fit naturally into a layered stack:

KBC Bank & Insurance Leuven, Belgium 2023 - Present

Python OpenAI Anthropic Open Source

AWS Bedrock LangGraph Python AgentOps Production