Debugging
15 articles about debugging.
Don't Trust Agent Self-Reports - Verify with Screenshots
Why AI agents report success even when they fail, and how screenshot verification after every action catches errors that self-reports miss.
The Irony of AI Automation - Debugging Skills Takes Longer Than the Original Task
It built a skill that posts to Reddit every hour on a cron job. Now I spend more time debugging the skill than doing the thing it was supposed to automate.
Error Handling in Production AI Agents - Why One Try-Except Is Never Enough
Why a single broad try-except catches everything and tells you nothing. Production AI agents need granular error handling with different recovery strategies.
Forgiveness in Error Handling - Why Agent Recovery Matters More Than Prevention
Graceful recovery in AI agents matters more than trying to prevent every error. Learn why forgiveness-first error handling builds more resilient desktop automation.
The 2AM Debugging Session - What AI Agent Development Actually Looks Like
Building AI agents isn't glamorous demo videos. It's late-night debugging of screenshot pipelines, accessibility tree parsing, and pixel-level click accuracy.
LLM Observability for Desktop Agents - Beyond Logging Model Outputs
Traditional LLM observability focuses on model outputs. For desktop agents, watching what the agent actually does on screen - logging actions, not just reasoning - matters more.
How to Debug MCP Servers That Stop Working
MCP servers break silently. Check the initialize handshake, restart the server process, verify the transport layer, and inspect Claude Desktop logs.
How to Monitor What Your AI Agent Is Actually Doing
Tool call logs look clean even when the agent is clicking on elements that do not exist. Screen recording is the missing observability layer for AI agents that interact with desktop and browser UIs.
Open Source AI Wearables Beat Closed Source - You Can Actually Debug Them
Why open source AI wearables like Omi give you the power to debug issues yourself instead of waiting in a closed-source support ticket void.
Optimizing Multi-Step Agents - Keeping a Running Log to Prevent Action Loops
Multi-step AI agents often repeat actions they already completed. The fix is simple - maintain a running log of completed steps so the agent knows what's done.
Opus 4.5 vs 4.6 for SwiftUI Debugging - How 4.6 Diagnosed a Constraint Loop Crash
Claude Opus 4.6 diagnosed a SwiftUI constraint loop crash that had been crashing for weeks - a problem Opus 4.5 could not solve. Here is what changed.
The Engineer's Trap - Optimizing Everything Like Debugging Code
Software engineers try to optimize meditation, relationships, and life like debugging code. Sometimes the best approach is to stop optimizing and let things work.
Recompiling Frustration Into Useful Output - The Emotional Cycle of Agent Development
Debugging AI agents is an emotional process. Learn how to channel frustration into productive debugging output and better agent development practices.
Staying Technically Sharp While Directing AI Agents Full-Time
How directing AI agents full-time erodes your hands-on debugging skills, and practical strategies to stay technically sharp while leveraging AI for productivity.
Building Visual Wrappers for Claude Code - Why Some Developers Go Native
Claude Code's terminal interface works but debugging AI agent actions is easier with a visual UI. Some developers build native macOS wrappers with SwiftUI for better observability.