Guides and insights on AI agent observability.
Beyond the obvious risks, running AI agents without observability creates invisible costs: wasted compute, slow debugging, opportunity cost, and eroded trust.
AI agents can fail silently, burn budgets, or cause compliance violations without anyone noticing. Here's why monitoring is insurance, not overhead.
Step-by-step tutorial showing how to add production-grade observability to AI agents with the Canary SDK. Works with OpenAI, Anthropic, any LLM provider.
Detailed comparison of Canary and LangSmith for AI agent tracing and observability. Framework lock-in vs framework-agnostic monitoring.
Honest comparison of Canary and Datadog for AI agent monitoring. Why purpose-built observability beats general APM for agents.
A practical guide to LLM cost monitoring for AI agents. Learn how to track token usage, set budgets, and avoid runaway costs in production.
Why traditional logging fails for AI agents in production. Learn what you actually need to debug agent failures.
How to implement distributed tracing for multi-agent AI systems. Covers trace propagation, parent-child relationships, and debugging.
The essential metrics for monitoring AI agents in production: cost per task, latency, quality score, tool reliability, and token efficiency.
Your AI agents are running in production. But do you know what they're actually doing? A practical guide to monitoring agents.