[HN] Why most general-purpose Agents fail and why I'm avoiding LLM "reasoning"

Summary

This article posits that the limitations of general-purpose AI Agents are directly tied to the current, nascent state of Large Language Models (LLMs). The author likens present-day LLMs to early, bulky steam engines, capable only of specific, limited tasks, placing them in a "cottage industry" phase of AI development. Consequently, the future success of Agents hinges entirely on substantial advancements in LLM capabilities, leading the author to currently avoid relying on LLM "reasoning."

Continue Reading

Explore related coverage about community news and adjacent AI developments: [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT, [r/LocalLLaMA] karpathy / autoresearch, [r/ML] [R] Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis (236 occupations, 5 US metros), [r/ML] Building behavioural response models of public figures using Brain scan data (Predict their next move using psychological modelling) [P].

Related Articles

• [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT

  March 29, 2026

• [r/LocalLLaMA] karpathy / autoresearch

  March 10, 2026

• [r/ML] [R] Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis (236 occupations, 5 US metros)

  April 7, 2026

• [r/ML] Building behavioural response models of public figures using Brain scan data (Predict their next move using psychological modelling) [P]

  April 5, 2026