debugging

LangChain has added a capstone project to their LangSmith Essentials course, providing more opportunities to practice debugging and tracing skills in AI agent engineering.

A visual programming environment allows users to compose AI agents with better composability and observability, including a kit of parts and an embedded agent for easier debugging and self-improvement.

A technical investigation into a crash in an iOS virtual machine project that uses UNIX domain sockets for TTY emulation, focusing on inode number mismatches during SSH connection initialization.

An author shares their experience with a tiny e-ink reader, encountering display bugs after installing open-source firmware, and using GPT-6 Astra in Codex to diagnose and fix the issues.

This article outlines best practices for optimizing Tokio applications in Rust, covering principles like latency vs. throughput and debugging strategies.

The author details fixing a green/pink video bug on an NZXT Signal 4K30 capture card caused by an RGB vs. YUV mismatch, with assistance from Claude AI for firmware investigation.

The author describes frustrating bugs in AI applications that are built and tested but fail to execute effectively in production, such as features targeting wrong users or not firing due to environmental conditions, and asks for methods to catch these issues.

The article details the debugging process for a crash in a program running under Wine, using tools like Valgrind and rr to identify a corrupted CALL instruction in libpng.

Toone is an AI workspace that enables building, running, and debugging complex workflows in natural language, with support for OpenAI and Anthropic models.

Building your own AI agent toolkit isn't complex; the minimal version includes tool schema validation, structured output parsing, and failure retries, but its true value lies in debuggability, allowing you to pinpoint issues through logs.

CopilotKit Inspector is an open-source debugging tool for AI agents that monitors interactions, helps reproduce failures, and uses AG-UI protocol to turn insights into agent improvements.

A user discusses strategies to debug AI agent systems in production where all indicators show success but outcomes are incorrect, seeking community advice on evidence and methods for diagnosis.

AI coding agents are proficient at generating code but struggle with debugging, leading to increased bug counts despite faster code production, as illustrated by personal experiences with Claude.

The article demonstrates how to use Wrapture, a developer tool, to identify slow code in Flask applications by providing instrumentation and timeline analysis for pinpointing performance bottlenecks.

The author built a local browser-based tool called Traser to help engineers diagnose runs that complete without errors but produce wrong outcomes by comparing execution steps and providing evidence-based investigation points.

The article explains that ACPI SystemIO conflict warnings are often misinterpreted as firmware bugs, detailing how ACPI operation regions and mutexes are used to handle hardware access conflicts.

The tweet discusses how improving trace quality by showing sequential LLM and tool calls instead of nesting them makes debugging and enhancing AI agents easier.

The author discusses the challenges of debugging AI agents, emphasizing observability issues and questioning current evaluation methods in production.

A developer discusses the lack of suitable observability tools for AI agents, expressing disappointment with existing solutions like Opik and hoping for a service that supports OpenTelemetry for analyzing agent sessions and failure modes.

The Rust debugging survey 2026 results reveal that over half of Rust developers do not currently use debuggers, with print debugging and the dbg! macro being the most common methods.