Quality SLOs: KPIs and NFRs as Measurable Targets
Explore how to define quality SLOs as explicit contracts incorporating KPIs and non-functional requirements to govern AI systems. Learn to measure task success, grounding, safety, latency, and cost constraints, enabling evidence-based release decisions and operational governance for non-deterministic AI workflows.
The integration packet defined what evidence a tool call would produce. It did not define what counts as an acceptable outcome or when a release should be allowed to proceed. In a non-deterministic system, quality should be defined as an explicit contract expressed through quality SLOs, rather than reduced to a single accuracy metric. A quality SLO identifies the person or team responsible for it, the measurement method, and a phase gate that can block release or trigger a predefined degraded mode.
The system retrieves policy and procedure content and uses MCP tools to read and, with approval, write updates to systems of record. Its business KPIs include throughput, time-to-decision, and reduced rework. Its NFRs include auditability, privacy, and latency. Those constraints mean the system has to be correct for the right reasons, in a way you can evidence later. A demo that feels helpful is not evidence that retrieval is grounded, that tool use is constrained, or that the resulting decisions are auditable.
Treating quality as SLOs changes the architecture decision surface. Model choice, retrieval configuration, tool permissions, and the AI gateway’s enforcement points all become levers that can satisfy or violate a specific SLO. Aim for a small set of measurable targets across model, retrieval, and tool or agent behavior, each tied to an evidence source and a gate action. Govern releases by evaluation results, not anecdotal prompts, and make post-release monitoring able to detect when ...