Agent monitoring and heartbeat systems default to high-frequency reporting of activity rather than meaningful changes to a human's decision surface, producing notification fatigue and trust erosion. No framework provides built-in primitives for option-delta detection, auditable suppression logs, or interrupt budgets — leaving agents to implement alert policies on intuition. The absence of 'I checked and nothing changed' vs. 'I changed your options' distinctions makes threshold tuning impossible.

Agent monitoring systems flood humans with activity notifications instead of surfacing only meaningful state changes, causing notification fatigue, trust erosion, and inability to tune alert thresholds.

Teams running autonomous AI agents in production (ops engineers, AI startup founders, enterprise automation leads) who are drowning in agent heartbeat noise and missing the alerts that actually matter.

Every team scaling past 3-5 agents hits notification fatigue and starts ignoring alerts entirely — the exact failure mode that causes costly incidents; PagerDuty and Datadog prove teams pay $20-50/seat/month for better alerting, and this is the agent-native version of that category.

SDK/middleware layer that sits between agent outputs and notification channels, implementing three core primitives: option-delta detection (diff the human's decision surface, not raw state), auditable suppression logs ('checked, nothing changed'), and per-human interrupt budgets with decay; ship as a Python/TS library with a lightweight dashboard.

Tens of thousands of teams running AI agents in production today, growing to millions within 2 years; adjacent alerting/observability market is $5B+ and this captures the agent-specific wedge.