---
title: Grafana Labs Applies Reliability Engineering to AI Agent Behavior
url: https://www.dataloco.com/en/grafana-labs-applies-reliability-engineering-to-ai-agent-behavior
published: 2026-11-03T22:21:32+00:00
language: en
section: AI
source: https://www.infoq.cn/article/z8vQa2zzGyLiTxIZ8PXH?utm_source=rss&utm_medium=article
organizations: Grafana Labs, Anthropic, OpenTelemetry
publisher: Dataloco
---

# Grafana Labs Applies Reliability Engineering to AI Agent Behavior

Grafana Labs has introduced a method to measure the actual performance of AI agents by applying reliability engineering techniques. The company proposes using evaluations, service level objectives, and error budgets to monitor behaviors such as hallucinations, task completion, toxicity, and cost.

This approach addresses limitations in traditional observability, where metrics like response speed and token consumption do not indicate if an answer is correct, grounded in available information, or useful to the user. Grafana Labs evaluates specific behaviors and converts the results into service level indicators that can be measured against service level objectives.

Evaluations check if an agent interaction meets specific criteria, such as whether a response is grounded in context, if a user request was completed, or if the output contains toxic content, leaks personal information, or is affected by prompt injection. These checks can be performed by another language model acting as a judge or through deterministic checks like regular expressions.

Grafana Labs treats quality as multidimensional rather than using a single score. This allows teams to set independent goals for different behaviors, preventing critical failures from being hidden in an aggregate score. For example, a team might set a service level objective requiring 95 percent of conversations to be judged as completed within 30 days, with the remaining 5 percent serving as an error budget for unacceptable behavior.

This model helps teams decide when to experiment with prompts and models and when to prioritize quality improvements as the error budget is depleted. It is particularly useful for detecting gradual quality drift caused by model changes, prompt modifications, or shifts in user behavior that traditional alerts might miss.

Grafana Labs suggests establishing baselines through evaluations to recommend target values, which can be tightened as an agent matures. This work began from challenges encountered while monitoring Grafana Assistant. The company distinguishes between offline evaluations for validating changes in known scenarios and online evaluations for production interactions.

Limitations include the risk of false precision if the judge model misclassifies responses or if evaluations reward technically correct but useless outputs. Organizations still require representative datasets, clear definitions of acceptable behavior, and human review for ambiguous cases.

Other organizations are pursuing similar goals. Anthropic has developed formal evaluation practices using defined tasks and multiple scorers to measure consistency. OpenTelemetry is creating standardized semantic conventions for agent telemetry to provide a consistent basis for tracing, metrics, and logs across different frameworks and vendors.

## This story in other languages

- [한국어](https://www.dataloco.com/ko/grafana-labs)