Are You Tracking the Right Things?
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.
Monitoring Tells You If It's Running. Evaluation Tells You If It's Working.
Let's break down the difference:
Monitoring is about tracking the operational health of your application. It answers questions like:
- 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?
Evaluation is about assessing the quality and correctness of your agent's behavior. It answers questions like:
- 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?
The Dangerous Blind Spot
You can have a perfectly monitored system that is a complete failure from an evaluation perspective. Your dashboard could be all green:
- ✅ 99.99% uptime
- ✅ 500ms average latency
- ✅ 0 API errors
- ✅ Costs are within budget
But in reality:
- ❌ 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.
Your monitoring dashboard tells you that your system is running. It tells you nothing about whether your system is working correctly.
Prioritize Evaluation, Then Monitor
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:
- Operational Metrics: Latency, cost, throughput.
- Quality Metrics: Accuracy, compliance, helpfulness, safety.
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?
Top comments (0)