AI Monitoring
AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift.
/ quick answer
Standard APM misses what breaks AI systems. You need per-request traces with the full prompt and tool calls, cost and token counts, tool error rates, refusal and fallback rates, escalation rate, and sampled quality scoring. Silent degradation — output that is worse but still valid — is only visible if you sample and score continuously.
/ frequently asked
What is AI Monitoring?
Standard APM misses what breaks AI systems. You need per-request traces with the full prompt and tool calls, cost and token counts, tool error rates, refusal and fallback rates, escalation rate, and sampled quality scoring. Silent degradation — output that is worse but still valid — is only visible if you sample and score continuously.
What is an example of AI Monitoring?
An alert fires when escalation rate crosses 25% or when average tool retries per run doubles week-on-week.
Why does AI Monitoring matter?
AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift.
What should I learn after AI Monitoring?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Structured Output
Forcing AI responses into predictable schemas that software can use.
- →Automation Observability
Monitoring inputs, model calls, outputs, cost, latency, and failures across AI workflows.
- →Agent Handoff
Agent handoff is the controlled transfer of a task — with its context — from one agent to another agent or to a human.
- →Agent Cost Control
Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
Related workflows
Turn this into a repeatable process.
- →Monitor an AI System in Production
See quality, cost and failure drift before your users report it.
- →Build an Eval Suite Before Optimising Prompts
Stop guessing whether a change improved anything.
- →Competitor Price Monitoring
Track competitor pricing pages daily and alert on changes.
- →Ship an Autonomous Workflow Safely
Move an automation from human-triggered to autonomous without losing control.
Related tool stacks
The tools that run it in production.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
- →Agentic AI Ops Monitoring Stack
This stack uses an AI browser, an LLM, and an orchestration layer to autonomously monitor IT systems, identify anomalies from web-based dashboards, and generate actionable insights for operations teams. It enhances proactive problem resolution.
- →AI Security Stack
Least-privilege tooling, approval gates and audit trails for agentic systems.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
Related prompts
Reusable prompts for this job.
- →Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
Related use cases
How people apply it, and what came out.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
Comparisons & alternatives
Pick between the options.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.