Many people jump directly into building AI agents using frameworks like LangGraph, CrewAI, or other agent frameworks.
But before building an agent, it’s important to understand how an agent actually works under the hood.
One of the fundamental concepts to understand is the TAO Loop:
Thought → Action → Observation → Repeat
In this blog, I explain the TAO Loop in beginner-friendly language, with diagrams and a practical Kubernetes troubleshooting example showing how an agent thinks, uses tools, observes the results, and decides what to do next.
📌 Prefer video? I’ve also explained the concept here:
https://youtu.be/Ng1z3TTAghY?si=9fSSZoah6VGCoqo3
I’ll be covering concepts like AI Agents, Tool Calling, ReAct, MCP, GenAI, GPU Infrastructure, Python, System Design, and DevOps/SRE automation in my upcoming 90-Day Intensive Program:
Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers
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