The article describes an experiment where the author intentionally broke a browser agent by replaying correct model decisions through four faulty browser actions. A basic page check failed to detect half of the incorrect clicks and incorrectly flagged every successful click within an embedded page. The piece highlights the challenges of diagnosing and resolving such browser-related faults.
By Abdullahi Dattijo
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
The article discusses how an agent can continue to produce confident answers even when its tools fail, leading to a misleadingly positive dashboard display. It highlights the issue of agents generating responses from nothing after tool breakdowns, which can create a false sense of reliability. The piece serves as a cautionary note for developers deploying agentic AI systems.
By Benjamin Nweke
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 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
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
The article discusses a research paper suggesting that reward labels have minimal impact on how AI agents learn. The author tested this claim on three different models, finding that one model behaved exactly as the incorrect reward labels instructed. The piece highlights how a single model can be misled by flawed feedback.
By Abdullahi Dattijo
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 reports on an experiment where an AI agent was allowed to search for evidence whenever it chose. The agent discovered some papers that a standard search missed, but also lost some that the plain search had found, and overall the gains were insufficient to be trusted. The post examines whether agentic Retrieval-Augmented Generation (RAG) truly retrieves better evidence across 298 questions.
By Abdullahi Dattijo
The article describes a claim‑checker tool built for writing with Codex that successfully identified the correct passage but failed to detect a mismatch, illustrating the limitations of rule‑based checks. The audit highlights where these automated checks break down and provides insights into improving claim verification processes.
By Benjamin Nweke
arXiv:2610.18579v1 Announce Type: new
Abstract: System One models choose an agent's next action by scoring a supplied set of options in a single pass. This makes them fast and cheap, but when they ch...
By Yubo Li, Ramayya Krishnan, Rema Padman
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