Monitoring vs. Evaluation: The Critical Distinction Most AI Devs Miss

Are You Tracking the Right Things? ## Monitoring Tells You If It's Running. Evaluation Tells You If It's Working. ## The Dangerous Blind Spot ## Prioritize Evaluation, Then Monitor In the world of DevOps and SRE, we're obsessed with monitoring. We track latency, error rates, CPU utilization, and requests per second. These metrics are essential for understanding the health of our systems. Naturally, when we started building AI agents, we applied the same mindset. We created dashboards to monitor our LLM API costs, token counts, and API error rates. But this is a critical mistake. For AI agents, monitoring is not the same as evaluation, and confusing the two can lead to a false sense of security. Let's break down the difference: Monitoring is about tracking the operational health of your application. It answers questions like: Evaluation is about assessing the quality and correctness of your agent's behavior. It answers questions like: You can have a perfectly monitored system that is a complete failure from an evaluation perspective. Your dashboard could be all green: Your monitoring dashboard tells you that your system is running. It tells you nothing about whether your system is working correctly. For AI agents, evaluation is the more important discipline. You must first have confidence that your agent is behaving as intended. Only then should you focus on optimizing its performance and cost. The ideal approach, of course, is to integrate both. A comprehensive AI observability platform should give you a single pane of glass that shows you both: But if you have to choose where to start, start with evaluation. It's better to have a slow, expensive agent that works correctly than a fast, cheap agent that is silently causing harm to your users and your business. Stop conflating monitoring with evaluation. They are two different disciplines, and for AI agents, evaluation is the one that truly matters. To implement both monitoring and evaluation, Noveum.ai's LLM Observability Platform provides a unified dashboard for operational metrics and quality evaluation. How does your team distinguish between monitoring and evaluation for your AI apps? Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - How many requests did we process? - What was the average latency? - How many times did the OpenAI API return a 500 error? - How much did we spend on tokens today? - Did the agent actually solve the user's problem? - Did it follow the instructions in its system prompt? - Did it provide factually accurate information? - Did it violate any compliance or safety rules? - ✅ 99.99% uptime - ✅ 500ms average latency - ✅ 0 API errors - ✅ Costs are within budget - ❌ 15% of the agent's responses are factually incorrect. - ❌ 10% of interactions violate your company's brand voice guidelines. - ❌ 5% of conversations expose sensitive user data. - Operational Metrics: Latency, cost, throughput. - Quality Metrics: Accuracy, compliance, helpfulness, safety.