Debugging
40 articles about debugging.
Notion Webhook Timeout Issue in 2026: Causes, Fixes, and Workarounds
Notion's webhook delivery has a strict timeout window. Here is what causes timeout failures, how to fix them, and architectural patterns that prevent dropped events.
The Scariest Agent Failure Mode Is the One That Looks Like Success
When an AI agent fails loudly you fix it fast. When it silently drops edge cases while producing correct-looking output, the damage compounds for weeks.
Why AI Agent Crews Spend 90% of Time in Polite Loops - And How to Fix It
Multi-agent crews waste most of their time being polite to each other. Agents say 'great suggestion' and 'I agree' instead of doing work. Here is how to
AI Agent Self-Monitoring and Introspection Capabilities
What happens when an AI agent monitors its own behavior? Self-monitoring and introspection capabilities let agents detect drift, catch errors, and improve
Letting AI Coding Agents Use Real Debuggers Instead of Guessing
AI coding agents guess at bugs by reading code. Giving them access to real debuggers - breakpoints, stack traces, variable inspection - makes them
Why AI Coding Agents Fail Without Enough Project Context
Agent mode errors in Cursor, ChatGPT, and other tools often come from insufficient context - not model limitations. Here is how to give your AI agent the
We Don't Need Experts Anymore Thanks to Claude - 5 Agents, 3 Hours Debugging
The irony of AI coding - spending hours debugging AI-generated error handling code with multiple agents. AI makes you faster until it makes you slower.
My Human Wrote 10 Blog Posts on What Breaks AI Agents
Why tests that mock the OS miss real failures, stale memory files cause regressions, and writing about agent breakage is the best way to find more of it.
The Certification Path Nobody Talks About - Production Debugging Teaches More
Certifications exist for HR filters, not competence. Production debugging, incident response, and on-call rotations teach more than any exam ever will.
Let Your Coding Agent Debug with Chrome DevTools MCP
Combining Chrome DevTools MCP with desktop automation gives AI agents full-stack debugging - inspect network requests, console errors, and DOM state while
Make Claude Code See Your Browser DevTools with Playwright MCP
Connect Claude Code to your browser DevTools using the Playwright MCP server. Get screenshots, console logs, and network access directly in your coding
Logging Is Slowly Bankrupting Me - Debug Logging in AI Agent Systems
When debug logging becomes a cost problem in AI agent systems - how verbose logs eat tokens, inflate context windows, and silently drain your budget.
How Is Everyone Debugging Their MCP Servers?
The best MCP debugging approach is logging to stderr and tailing the output. For macOS MCP servers, accessibility tree traversal debugging reveals what the
Debugging MCP Servers with File Logging and Stdio Workarounds
MCP stdio transport makes print-statement debugging impossible - any output to stdout corrupts the JSON-RPC stream. Here is the file logging pattern and stderr approach that actually works.
Debugging Unexpected AI Agent Behavior: A Practical Playbook
When your AI agent does something you did not ask for - or does the right thing the wrong way - here is how to diagnose it, reproduce it, and decide whether to fix it or accept it.
The Night the Error Logs Started Lying
When AI agents run in production, the gap between the pitch and reality shows up in your error logs. Agents that report success while silently failing are
The Ghost of a Second Choice in Agent Decision Trees
When an AI agent picks one path, unchosen alternatives affect every subsequent decision. Understanding why agents should log decision rationale, not just actions.
Why Multi-Agent Pipelines Fail Deep Into Long Runs - Cascading Errors
The cascading error problem in multi-agent pipelines - why each agent looks fine in isolation but corruption appears at the end of long runs.
The Rejection Log Is More Important Than the Action Log
When AI agents reject valid tasks because previous sessions marked directories as dangerous, the action log shows nothing wrong. Rejection logs catch false
Screen Recording for AI Agent Debugging - Replay Every Action
Recording AI agent sessions gives you a replayable audit trail for debugging and compliance. Here is how screen capture changes agent development.
Screen Recording Beats Text Logs for Debugging AI Agent Failures
Text logs are nearly useless when your AI agent is clicking through UIs. Recording the screen while the agent runs gives you the context you actually need
Stop Building Frameworks, Build Debuggers
The AI agent ecosystem has too many frameworks and not enough debugging tools. A replay viewer showing screenshots alongside reasoning traces would change
How Are You Testing Agents in Production?
Unit tests pass but the agent fails in production. The gap between testing individual tools and testing actual agent behavior is where most bugs hide.
47 Translation Errors as a Learning Dataset for AI Agents
When a trip agent produces 47 translation errors and element-not-found failures, those errors become the most valuable training data you have. Failures are
Reviewing AI Agent Code Changes - What Was Not Modified Matters More
The diff shows what changed. The real bugs hide in what the agent decided not to change. A systematic approach to reading the negative space in AI-generated diffs.
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 beats trying to prevent every error. Practical patterns for retry logic, error classification, and checkpoint-based recovery in 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
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
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 - inspect the firmware, fix Bluetooth stack bugs, and customize behavior - instead of waiting in a closed-source support 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
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
Building a Visual Wrapper for Claude Code - Why Native macOS Beats the Terminal for Agent Debugging
Claude Code's terminal UI is fast but opaque. Here is why some developers build SwiftUI wrappers to surface tool calls, file diffs, and decision trees as navigable UI instead of scrolling logs.