Day-2 operations, observability, and autonomous runbooks
for AI-native estates

Application APM and model observability live in separate tools—with no unified view from API to inference to agent action

Runbooks are static documents instead of executable workflows that can invoke remediation, rollbacks, or human escalation

GPU, token, and inference costs surface only at billing time—too late to tune prompts, models, or routing policies

Alerts fire on infrastructure thresholds but miss drift, hallucination spikes, and agent loop failures

On-call engineers lack AI-specific context—retrieval traces, tool calls, and eval history—when triaging incidents