Grafana LabsIvana Huckova8 min readintermediate
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What if your agent's hallucinations had a budget? How to start using SLOs for agent behavior
Summary
This article proposes applying Service Level Objectives (SLOs) and error budgets to AI agent behavior to measure and manage quality beyond traditional metrics. It suggests using "evaluations" (often by other LLMs or deterministic checks) to quantify agent behaviors like groundedness or fulfillment, turning qualitative aspects into measurable metrics. These metrics then enable the use of SLOs and…
- Traditional observability metrics (latency, errors) don't assess AI agent quality like groundedness or fulfillment.
- Evaluations (model-based or deterministic) quantify agent behavior, turning qualitative aspects into measurable scores.
- Agent quality is a set of independent behaviors (e.g., groundedness, toxicity, cost), each needing its own measurement.
- SLOs and error budgets provide a framework to manage agent reliability, allowing experimentation within a defined budget.
Engineers building and operating AI agents should care about this, as it provides a structured, data-driven approach to measure and manage agent quality and reliability, moving beyond subjective assessments.
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