FUNDING · COMPANIES · #2116
Grafana applies SLOs and error budgets to AI agent behaviour
Grafana Labs proposes using behavioural evaluations, SLIs, SLOs and error budgets to turn agent issues like hallucination, task fulfilment, toxicity and cost into measurable targets that can be monitored over time. The company recommends converting automated or LLM-based evaluations into SLIs and using SLO targets and error‑budget burn to guide experimentation, detect gradual quality drift, and prioritize fixes.
KEY POINTS
- Grafana Labs proposes using behavioural evaluations, SLIs, SLOs and error budgets to turn agent issues like hallucination, task fulfilment, toxicity and cost into measurable targets that can be monitored over time.
- The company recommends converting automated or LLM-based evaluations into SLIs and using SLO targets and error‑budget burn to guide experimentation, detect gradual quality drift, and prioritize fixes.
- It adapts established SRE observability tools to measure and manage AI agent behaviour, giving teams a practical framework to balance experimentation and reliability.
WHY IT MATTERS
It adapts established SRE observability tools to measure and manage AI agent behaviour, giving teams a practical framework to balance experimentation and reliability.