The article titled "Your Agent’s Tools Can Make Decisions, Too" discusses decision models as tools and offers a hands‑on guide for their implementation. It highlights how agents can leverage these tools to make informed choices. The post was originally published on Towards Data Science.
By Shuai Guo
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 paper "Signals: Continuous Diagnostics of AI Agents" introduces a method for diagnosing problems in AI agents by analyzing agentic traces. It defines 45 problem types across eight families, categorizes findings into engineering issues called Signals, and assesses their severity. The method was evaluated on public agent executions, achieving a 91.43% F1 score in matching Signals to reference issues, outperforming a one-pass LLM control by about 18 percentage points.
By Amey Rambatla, Vatsal Goel, Sagar Gaur, Siddhant Loya, Anurag Mishra, Asif Mohammad
AI agents can make incorrect decisions or waste resources even when their tools and infrastructure work normally. Their nondeterministic behavior makes these problems difficult to reproduce and diagno...
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 paper argues that artificial agentic systems, which operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time, should be evaluated through systematic observation, perturbation, and interpretation of their actions rather than solely on performance outcomes. It draws on lessons from behavioral sciences to motivate this position and proposes a research agenda that includes methods for recovering decision strategies from action sequences, constructing environments that isolate behavioral differences, and probing emergent dynamics in multi‑agent systems. These directions aim to establish a rigorous science of AI behavior.
By Manuel Cherep, Nikhil Singh, Pattie Maes