Monitoring
19 articles about monitoring.
How to Find the Conversations Where Your AI Agent Fails and Users Abandon
Your AI agent works 95% of the time, but the 5% where it fails silently causes users to leave. Here is how to instrument, detect, and triage those conversations systematically.
AI Agents for Crypto: Monitoring and Alerts, Not Autonomous Trading
The real utility of AI agents in crypto is monitoring portfolios, tracking alerts, and flagging anomalies - not making autonomous trading decisions. Here's
Three Patterns Where AI Agents Silently Abandon Work
AI agents can silently abandon tasks through slow drift, false completion reports, and stale maintenance claims. Learn to detect and prevent these task
Detecting Signals - Edge Cases in Production Agent Work
Production AI agents need to detect weak signals in noisy environments. The edge cases that break agents are rarely dramatic - they are subtle shifts in
The Echo Chamber of Error Correction - Use a Separate Validation Pipeline
When an agent validates its own work, it uses the same reasoning that produced the error. A separate validation pipeline with different assumptions catches
Nobody Explains How to Make Agents Run Reliably
Making AI agents reliable requires structured state management, proper error recovery, and continuous monitoring - not just better prompts. Here is what
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.
Monitoring Autonomous AI Agents - Spending Caps, Action Logs, and Notification Triggers
Letting an AI agent run overnight without guardrails is how you wake up to a $500 API bill and 200 unintended actions. Here is how to set up proper monitoring.
Monitoring Multiple AI Agents Running in Parallel - Visualization and Conflicts
Running multiple AI agents simultaneously is powerful but creates new problems. Here is how to monitor them, detect conflicts, and keep them from stepping
The Observer Hierarchy: Building Layered AI Agent Safety Beyond First-Order Guardians
One guardian watching one agent is not enough. Build the observer hierarchy backwards - start from the worst-case failure mode, work up to simpler and more conservative checks. Here's the five-layer production pattern.
The Real Friends We Made Were in Downdetector
When cloud services go down, Downdetector becomes the real standup meeting. Why monitoring AI agent dependencies matters more than you think.
Suppressed 34 Errors in 14 Days - When to Escalate Regardless of Severity
When the same error happens three times with the same root cause, escalate it regardless of severity. Suppressing 34 errors in 14 days taught us that
Why Every AI Agent Team Needs a Cron Job Audit Trail
Scheduled AI agent tasks fail silently more often than you think. A cron job audit trail catches missed runs, silent errors, and drift before they become
Why Uptime Percentages Are Misleading for AI Agent Deployments
99.9% uptime means nothing if all your agents fail at the same time. Co-failure is the hidden metric that matters more than uptime for AI agent deployments.
Proactive AI Agents That Help Without Being Asked
How to build AI agents that detect problems and act on them before you ask - including concrete trigger implementations, risk tiering, and the trust gradient that makes proactive automation safe.
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 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
Reviewing What Your AI Agents Did Overnight - The Green Dashboard Problem
AI agent dashboards often show everything green until you click in. Learn how to build meaningful morning review workflows that surface real issues instead
Using Ollama for Local Vision Monitoring on Apple Silicon
Local vision models through Ollama handle real-time monitoring tasks like watching your parked car. Apple Silicon M-series makes local inference fast enough