This series documents my learning journey with AI agents, informed primarily by hands-on experience using Codex across a range of projects.

The field is advancing—and the ways we work with agents are changing—so quickly that I often feel as though I am struggling to keep up. For me, writing this blog is a way to capture the experiences and observations I gather along the way. It helps me synthesize and share ideas I find useful, while also providing a constructive way to acknowledge and manage the anxiety that comes with such rapid change.

AI Agents 101

This section provides a foundational map of AI agents, covering the essential concepts needed to understand how they work. The initial drafts were created with Codex and will be expanded and refined over time as I gain new knowledge and practical experience through challenges at work.

  1. Why Agents, Why Now

  2. From Prompt Engineering to Agent Systems Engineering

  3. The Core Agent Model

  4. Agent Loop and Runtime Design

  5. Context and Memory

  6. Tools, MCP, Skills, and Plugins

  7. Orchestration Patterns

  8. Data Layer and Enterprise Integration

  9. Safety, Security, and Governance

  10. Evaluation and Observability

  11. Production Agent Architecture

  12. Agentic UX

  13. Coding Agents as a Case Study

  14. Standards and Ecosystem

Deep-Dive Topics

This section explores the topics that interest me most and that I want to study more deeply with Codex.

My Working Experience with AI Agents

This section captures my practical experience working with AI agents. It includes everything from concise tips and tricks to longer posts that bring together the skills and workflows I use in my day-to-day work.