AI Agent Observability Insights

AI agents fail in unpredictable ways. Learn how real-time observability prevents runaway costs, hallucinations, and silent failures before they reach your users.

Most teams log prompts and responses and call it observability. Real LLM observability tracks cost per session, token drift, latency percentiles, and PII exposure.

LLM API costs compound fast. Learn the patterns that cause runaway spend and how automated budget caps, anomaly detection, and Cost Autopilot keep costs predictable.

Your n8n workflows call OpenAI or Anthropic — but do you know what each run costs? This step-by-step guide shows how to add cost tracking in under 5 minutes.

OpenAI and Anthropic have spending limits. So why do teams still get blindsided by cost spikes? Because provider limits cap the total — not per agent, per session, per workflow.

LangSmith, Helicone, Langfuse, or AgentShield? A practical comparison of the leading AI agent monitoring tools in 2026 — features, pricing, and when to use each.

Most AI agent cost problems come from the same five patterns. Here's what they are, how much they cost, and how to fix each one.

A step-by-step guide to setting budget limits on your AI agents — the difference between provider total caps and per-agent caps, and how to set both.