Skip to content

Agents 101

The Cortex — Architecting Memory for AI Agents

Part 2 of the Engineering the Agentic Stack series

State is what separates a chatbot from an agent. Without memory, every interaction starts from zero — the agent cannot pause and resume, cannot learn from past sessions, cannot personalize. In Part 1, I covered the cognitive engine that decides how an agent thinks. This post tackles the infrastructure that determines what it remembers.

I'll walk through the memory architecture of the Market Analyst Agent, showing how hot and cold memory layers work together to support checkpointing, pause/resume workflows, and cross-session learning — and why a third tier of document-based memory is becoming essential for agents that manage their own knowledge.

The Cognitive Engine: Choosing the Right Reasoning Loop

Part 1 of the Engineering the Agentic Stack series

Building production AI agents is no longer about prompt engineering—it's about system engineering. The difference between a demo that impresses and a product that delivers comes down to one critical decision: how your agent thinks.

This post introduces three reasoning loop architectures and shows you how to choose between them. I'll use a production-grade Market Analyst Agent as the running example, with code you can use today.