MLA 028 AI Agents

MLA 028 AI Agents

From Machine Learning Guide by OCDevel

February 22, 2026 · 38 min · Season 1 · Episode 64

About this episode

This episode discusses the differences between AI agents and chatbots, focusing on the ReACT loop and the evolving market for automation tools.

AI agents differ from chatbots by pursuing autonomous goals through the ReACT loop rather than responding to turn-based prompts. While coding agents are currently the most reliable due to verifiable feedback loops, the market is expanding into desktop and browser automation via tools like Claude co-work and open claw. Links Notes and resources at ocdevel.com/mlg/mla-28 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want Fundamental Definitions Agent vs. Chatbot: Chatbots are turn-based and human-driven. Agents receive objectives and dynamically direct their own processes. The ReACT Loop: Every modern agent uses the cycle: Thought -> Action -> Observation . This interleaved reasoning and tool usage allows agents to update plans and handle exceptions. Performance: Models using agentic loops with self-correction outperform stronger zero-shot models. GPT-3.5 with an agent loop scored 95.1% on HumanEval, while zero-shot GPT-4 scored 67.0%. The Agentic Spectrum Chat: No tools or autonomy. Chat + Tools: Human-driven web search or code execution. Workflows: LLMs used in predefined code paths…

Topics covered

  • AI agents
  • autonomous goals
  • ReACT loop
  • coding agents
  • desktop automation
  • browser automation

Keywords

  • AI agents
  • chatbots
  • ReACT loop
  • autonomy
  • automation tools
  • LangGraph
  • CrewAI

Mentioned in this episode

Organizations: Claude, LangGraph, CrewAI

Products: GPT-3.5, GPT-4

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