Observability
19 articles about observability.
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.
Tracking AI Agent Reputation Across Multiple Dimensions
A single reliability score for AI agents is misleading. Agent reputation needs to track speed, accuracy, cost efficiency, and failure patterns separately to
Data Quality as a Moral Imperative for AI Agent Analytics
A stats pipeline counting deleted posts inflated engagement numbers by 40 percent. Data quality in AI agent analytics is not just a technical problem - it
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.
The Five Logs Every Cron-Scheduled AI Agent Needs
Actions, rejections, handoffs, costs, and verification - the five essential logs for cron-scheduled AI agents. How a cost log exposed 40% waste in our agent
Rolling Your Own Agent Logging - SQLite Locally, Postgres in the Cloud
Building custom logging for a desktop agent revealed that 40% of token spend went to retries from the model misunderstanding accessibility tree data.
The Simplest Way to Log Parallel Sub-Agent Conversations
When running 5+ AI agents in parallel with an orchestrator, having each sub-agent write its conversation to a file is the most reliable logging approach.
How to Monitor AI Agent Health in Production
Heartbeats, error rates, latency tracking, and alerting on silent failures - a practical guide to monitoring AI agents running in production environments.
Agent Logs as Open Letters to Nobody - Why Unread Documentation Has Value
Most agent logs are never read by a human - but they still shape how AI systems evolve. Here's why structured logging is worth doing even when nobody looks.
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
What's the Difference Between Trusting an AI Agent and Verifying One?
Trust means believing the agent will do the right thing. Verification means checking that it did. For desktop agents, verification wins every time.
AI Agent Decision Logging That Nobody Reads - The Audit Trail Gap
Complete audit trails are useless without attention. Why AI agent logging needs to be paired with automated review, not just stored. The gap between
AI Agents Lie About What They Did - Why You Need Action Verification
LLMs confidently report failed actions as successful. You need accessibility tree snapshots and state verification to know if your agent actually did what
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
127 Silent Judgment Calls Your AI Agent Made in 14 Days
Logging every silent decision an AI agent makes reveals 127 judgment calls in 14 days you never saw. Why decision transparency matters for agent trust.
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.