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 .
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.
The article "Bug Detection Blind Spots in AI Coding Harnesses (GStack and Beyond)" reports on 28 debugging experiments that show AI coding tools struggle more with missing information than with code complexity. It highlights that these tools exhibit blind spots when key details are absent, affecting their debugging performance.
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...
The article discusses the essential skill of effectively instructing coding agents and verifying their changes. It highlights that while line‑by‑line code review is one method, it is not the most efficient way to validate software changes. The focus is on confidently guiding agents and confirming correct implementation without exhaustive inspection.
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 .
A minimal loop with real API calls, validation, compact outputs, and trace evidence before adding an agent framework The post I Built a Tool-Calling Agent in Python. Here’s How I Debugged It appeared first on Towards Data Science .
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.
Increase the effectiveness of your coding agents through end-to-end testing. The post How to Run End-to-End Tests with Claude Code appeared first on Towards Data Science .
arXiv:2605.29442v2 Announce Type: replace-cross Abstract: AI coding agents increasingly act directly within software environments, yet existing analyses of their failures rely on benchmark trajectori...
The paper introduces a dataset of the complete development history of a 21,000-line Python tool built entirely by Claude AI, accompanied by two code‑provenance tracing tools and three taxonomies for instruction intent, commit provenance, and response reliability. Analysis reveals that user CLI instructions differ from IDE‑chat instructions, focusing more on comprehension, planning, and consultation; code development is largely proactive; 14.3% of AI code‑generation events contain errors later caught by the AI‑authored test suite; and roughly one in four to five of the AI’s interactive responses contain factual errors.
How OpenAI uses chain-of-thought monitoring to study misalignment in internal coding agents—analyzing real-world deployments to detect risks and strengthen AI safety safeguards.
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 .