The canonical position for observing AI and agent system outcomes.
A cross-cutting coordinate for the observability function focused on outcomes — the layer that makes it visible whether AI systems, agents, or automated workflows are achieving the results they were built for.
Coordinated sets this position belongs to — the coverage it extends. Counts are the live cluster size in the graph.
Architectural context
Outcome · Cross-Vertical · 2 compound moats. Cross-cutting: Outcome, Observability.
Layer position: Cross-cutting
Why this is canonical
Standard observability measures technical signals (latency, error rate, throughput); outcome observability names the higher-order function of measuring whether those systems are achieving their intended business or operational results. This coordinate sits at the intersection of AI observability and outcome-based accountability.
Where it fits
A few directions this coordinate opens —
Illustrative, not exhaustive — held as a transferable canonical position, open to the buyer's own use.