The article discusses how agentic AI is reshaping the analytics stack by taking over more execution tasks. It raises the question of which responsibilities should remain with human analysts versus AI agents and explores the importance of this distinction. The piece highlights the evolving role of AI in analytics and the need to define clear boundaries between human and machine work.
By Rashi Desai
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 describes an experiment where an AI agent was able to identify incorrect model decisions while interacting with a web page, yet failed to correct the errors. By replaying correct decisions through four intentionally flawed browser actions, the author demonstrates that a simple page check missed half of the erroneous clicks. This illustrates that detecting a fault and successfully recovering from it are distinct capabilities.
By Abdullahi Dattijo
The article explains how the five core assumptions of MLOps monitoring are violated when agents are deployed to production, leading to inherited signals that incorrectly mark failed runs as healthy. It highlights the specific ways in which agent-based systems disrupt traditional monitoring stacks and the implications for reliability and performance. The piece serves as a warning for practitioners transitioning from MLOps to AgentOps, outlining the critical monitoring gaps that arise.
By Mostafa Ibrahim
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
A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science .
By Abdullahi Dattijo
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 "Is Agentic AI Just Automation?" argues that many so‑called agents are merely flowcharts in disguise. It explains why this misconception exists and suggests what kinds of systems should be built instead to achieve true agentic AI.
By Prashant Mudgal
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.
The article explores the effects of removing a search box from an AI agent and instead providing it with typed tools, hard bounds, and a gate that it cannot bypass. It examines how the agent navigates a knowledge graph within strict limits and discusses findings from four models and one incorrect prediction regarding the value of this approach.
By Miodrag Cekikj
One near miss, four months of running agents, and the question almost nobody is asking: what are you supposed to do while the AI writes the code?
The post AI Made Me 5x Faster. It Also Made Me 5x Wors...
By Gursimar Singh
The article titled "Your Agent Doesn't Need More Freedom" discusses how agents have become proficient, but the real challenge lies in the surrounding environment. It highlights that the focus should shift from granting agents additional autonomy to addressing the complexities that exist around them. The piece underscores the importance of improving the context in which agents operate rather than simply increasing their freedom.
By Gursimar Singh