debugging

An engineering blog post from Buildkite details how a flaky test led to the discovery of a use-after-free bug in the redis-client Ruby library, describing the debugging process and root cause analysis.

A developer introduces TMDD/TTMDD, a pattern where code measures its own time and tokens to distinguish a working AI agent from a hung one.

This paper proposes a method for deterministic replay in AI agent systems, enabling reproducible debugging and analysis.

AgentDebugX is an open-source debugging framework for LLM agents that organizes failure debugging as a closed loop of detect, attribute, recover, and rerun, with a core component DeepDebug achieving strong attribution accuracy and repair capability on benchmarks.

A developer traces a week of messages from AI agents after they asked the same prospect the same question twice, revealing insights into agent coordination issues.

Libretto PR agents automatically investigate and fix failing Playwright scripts by opening GitHub pull requests with proposed code fixes.

Raymond Chen speculates on how a buggy control panel extension caused a crash by truncating a 64-bit pointer to 32 bits, likely due to incomplete code updates during 64-bit porting.

This post discusses the challenge of giving AI coding agents real production context, such as logs, metrics, and traces, to improve their debugging and fix suggestions, and asks the community for practical solutions.

SPINE is an agentic framework that systematically debugs and deploys bimanual robots, reducing reliance on expert calibration and improving operationalization success across platforms.

This paper introduces OAT, a lightweight failure attribution tool for LLM-based agentic systems that trains only on successful trajectories and uses neural controlled differential equations to detect error steps, outperforming expensive baselines by orders of magnitude in speed and accuracy.

This article from The Old New Thing investigates a common crash in the Windows display control panel caused by an invalid function pointer that was truncated to 32-bit and sign-extended. The author analyzes a crash dump to identify the root cause.

Presents Oat, a lightweight unsupervised method for identifying error steps in LLM-based agentic failure trajectories using neural controlled differential equations trained only on successful trajectories. It achieves 200-5000x speedup over prompting baselines with significant F1 improvements in in-domain and out-of-distribution settings.

Alvin Sng explains why their team moved away from using client SDKs for Stripe, WorkOS, and Slack, opting instead to call their REST APIs directly via a centralized wrapper. They argue that SDKs hide critical debugging details, are fragile in production, and encourage anti-patterns that are now more easily avoided with AI-assisted coding.

A reflective article on the challenges of building AI agents for small businesses, comparing the process to pouring water into a leaky cup, where errors and bugs cause inefficiencies, but systematic debugging and rule-writing gradually improve the system.

The article argues that while encrypted subagent prompts protect message contents, they still require a local audit trail for debugging and reconstructing agent actions.

Explores four ways an agent's write can silently disappear, with two detectable and two preventable issues.

A blog post from Inngest detailing how adding a second middleware corrupts TypeScript types due to a loophole in type constraint checking, and explaining the root cause involving optional properties and conditional types.

A user shares how an AI assistant named Sol helped debug a MacBook's random disconnection issue with a CalDigit TS5 dock, identifying that the problem occurred during heavy GPU loads when the dock couldn't supply enough power.

Sigwire is a live terminal dashboard that streams all signals on a Linux system using eBPF tracepoints, showing sender, target, signal type, and handler details without ptrace or process cooperation.

A comment on the challenge of understanding interactions between AI agents, where individual actions are visible but collective behavior is opaque until a failure reaches the customer.