Workflow
Monitor an AI System in Production
See quality, cost and failure drift before your users report it.
1 min readupdated 2026-08-01
/ quick answer
Trace everything, alert on behavioural metrics, and sample-score output continuously. See quality, cost and failure drift before your users report it.
See quality, cost and failure drift before your users report it. The problem it solves: AI features degrade quietly: still returning answers, just worse ones, with rising cost. Trace everything, alert on behavioural metrics, and sample-score output continuously. It runs in 6 steps, starting with trace every run: inputs, prompts, tool calls, outputs, tokens, latency, cost. This workflow node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Problem
AI features degrade quietly: still returning answers, just worse ones, with rising cost.
Solution
Trace everything, alert on behavioural metrics, and sample-score output continuously.
Steps
- 01Trace every run: inputs, prompts, tool calls, outputs, tokens, latency, cost.
- 02Instrument behavioural metrics: escalation rate, tool error rate, retries, refusals.
- 03Sample 1–5% of runs daily and score them with the eval rubric.
- 04Alert on deltas, not absolutes — week-on-week change catches drift earliest.
- 05Review the worst 10 traces weekly and convert each into an eval case.
- 06Keep a rollback path: pinned model version and previous prompt ready to restore.
Tools Used
Prompts Used
Related Dictionary
/ frequently asked
What does the Monitor an AI System in Production workflow do?
Trace everything, alert on behavioural metrics, and sample-score output continuously.
What problem does Monitor an AI System in Production solve?
AI features degrade quietly: still returning answers, just worse ones, with rising cost.
How many steps does Monitor an AI System in Production take?
6 steps. It starts with trace every run: inputs, prompts, tool calls, outputs, tokens, latency, cost. and ends with keep a rollback path: pinned model version and previous prompt ready to restore..
Which tools does Monitor an AI System in Production need?
It uses ai-observability-stack — each linked below with its own node.
↳ connected nodes
Dictionary↳ linked
AI Monitoring
AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift.
Dictionary↳ linked
AI Evaluation
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.
Tool Stack↳ linked
AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
Prompt↳ linked
Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
Comparison↳ linked
Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.
Use Case↳ linked
Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.