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
Oct 9
Stop Using AI. Start Hiring It.
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.
Simon Willison
Oct 28
Quoting Terk Terkowitz
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.
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
Sep 28
The AI That Learned to Understand Long After It Stopped Trying
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.
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
Oct 25
Agentic AI in Production: Why Your Agent Keeps Answering After Its Tools Fail
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.
Towards Data Science
Oct 4
How to Govern AI Agents
The article "How to Govern AI Agents" discusses strategies for overseeing a single AI agent and expanding those governance practices to manage a fleet of agents. It explores the challenges and solutions involved in scaling control mechanisms as the number of agents increases. The piece highlights practical approaches for ensuring reliable and ethical operation across multiple AI systems.
Towards Data Science
Oct 27
An AI Agent Can Spot a Mistake and Still Not Fix It
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.
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
Oct 9
Build Your First AI Agent with One Tool Call
The article titled "Build Your First AI Agent with One Tool Call" introduces beginners to AI agents, presenting the first part of a series. It focuses on the foundational steps required to create an AI agent using a single tool call. The post is published on Towards Data Science and serves as an introductory guide for newcomers to the field.
Towards Data Science
Sep 19
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...