Towards Data Science
Jun 3
What AI Agents Should Never Do on Their Own
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 .
Stop Giving Your AI Agent a Search Box and Start Giving It Typed Tools, Hard Bounds, and a Gate It Cannot Talk Past
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.
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Towards Data Science
Jun 21
Tool Calling, Explained: How AI Agents Decide What to Do Next
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 .
Towards Data Science
Aug 17
Webwright: Why AI Web Agents Should Write Code, Not Click
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.
Towards Data Science
Sep 3
How to Solve the Right Problem in the Age of Agentic AI
The article "How to Solve the Right Problem in the Age of Agentic AI" presents a practical framework aimed at reducing uncertainty before agents accelerate implementation. It offers guidance on identifying and addressing the most relevant problems in the context of increasingly autonomous AI systems.
Towards Data Science
3d ago
AI Made Me 5x Faster. It Also Made Me 5x Worse at My Job.
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...
Towards Data Science
Aug 27
How to Work with AI Coding Agents
The article "How to Work with AI Coding Agents" offers a practical guide aimed at improving code quality rather than merely increasing quantity. It focuses on strategies and best practices for effectively collaborating with AI coding tools to produce better code. The post was originally published on Towards Data Science.
arXiv AI
Aug 28
Learning the ARTS of Search for Automated Discovery
The paper introduces Agentic Reasoning for Tree Search (ARTS), a method that uses a reasoning language model to navigate the hypothesis‑experiment space in scientific discovery. Unlike traditional approaches that conflate hypothesis quality with execution quality and prune search logs, ARTS evaluates prior execution logs to distinguish implementation failures from poor hypotheses and selects the next hypothesis to pursue. By employing test‑time training to embed search‑tree knowledge into model weights, ARTS achieves a 15.3% relative improvement over leading algorithms on 22 benchmark tasks and enables smaller models like Qwen3‑4B to match or exceed the performance of larger closed‑source models at lower inference cost.
Simon Willison
Sep 7
Quoting Jakub Pachocki
Simon Willison quotes Jakub Pachocki, Chief Scientist at OpenAI, arguing that the strongest reason to rapidly train smarter AI models is the necessity of building defensive systems against the dangers posed by other AI. Pachocki stresses that powerful, aligned AI will be essential for securing infrastructure, protecting against rogue agents in real time, and inventing new protective measures, making this a primary focus of OpenAI’s deployment efforts. He cautions that the urgency of progress should not justify reckless behavior, noting that the seriousness of the stakes makes a reckless race forward absurd.
Towards Data Science
Jul 10
The Big Con of Agentic AI
What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
Simon Willison
Sep 12
Quoting Paul Ford
Simon Willison reflects on the evolving role of software developers in the age of AI, noting that while AI can produce high‑quality code, it also enables poor execution that leads to project failures. He argues that the industry is beginning to recognize the continued need for human collaboration and expertise to truly innovate. The piece highlights the tension between automation and the essential human element in software creation.
Towards Data Science
Aug 27
Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch
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.
Towards Data Science
Jun 23
The Era of No-Code AI: What You Need to Know
If you are a programmer and you don't feel "special" anymore, you are not alone The post The Era of No-Code AI: What You Need to Know appeared first on Towards Data Science .