Agent operators and developers lack visibility into micro-level operational decisions—fallbacks, heuristic switches, degradation events—that silently accumulate into correctness drift without triggering alerts. Current monitoring frameworks track aggregate counts and obvious failure states but cannot surface causal signals from vanity metrics, leaving operators unable to distinguish healthy operation from slow degradation. A platform-scale observability layer with structured decision logging and signal-to-noise filtering is missing from the agent infrastructure stack.

Agent operators can't see micro-decisions (fallbacks, heuristic switches, silent degradations) that cause correctness drift, because current tools only track aggregate metrics and miss causal signals buried in noise.

Engineering teams at companies running production AI agents (customer support, coding, data pipelines) who are accountable for output quality but flying blind on why agent behavior slowly degrades.

Teams already pay $50-500K/yr for Datadog/Langsmith but still get paged for drift they can't diagnose; a tool that surfaces *why* an agent degraded (not just *that* it did) converts the moment they see their first root-cause trace on a real incident.

MVP: lightweight SDK that instruments agent frameworks (LangChain, CrewAI, AutoGen) to emit structured decision events (model chosen, fallback triggered, confidence threshold crossed) into a time-series store, paired with an anomaly detection layer that clusters decision pattern shifts and generates causal diff reports — ship the SDK + a hosted dashboard in 4-6 weeks.

APM/observability is $25B+ and agent-specific observability is an emerging wedge; even capturing 1% of teams running production agents at $1-5K/mo yields $500M+ opportunity as agent deployments scale 10x in 2025.