How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
By Sara Nobrega
The article argues that AI agents have surpassed human capacity in code production, prompting a shift in perspective from merely using AI to actively employing it. It highlights how this development has altered the author's view on AI’s role in software development. The piece suggests that embracing AI as a collaborator is now essential.
By Gursimar Singh
The article discusses how engineers should view the lack of benefit from current AI tools as a finding rather than a failure. It encourages documenting these observations, sharing them with peers and managers, and actively seeking solutions. By explaining where AI falls short, engineers demonstrate the judgment expected of them.
Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .
By Maria Mouschoutzi
The article titled "The AI That Learned to Understand Long After It Stopped Trying" discusses a small, strange discovery in machine learning known as grokking. It highlights how this phenomenon involves an AI developing understanding after ceasing to actively try. The piece was originally published on Towards Data Science.
By Utkarsh Mangal
For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the program instead.
By Chien Vu Minh