Solution Capability · 6.2
Make production AI behavior visible through operational, quality, safety, usage, cost, and integration monitoring.
Teams cannot manage what they cannot see. Standard application uptime does not reveal whether an agent is completing tasks, using the right sources, escalating correctly, or creating avoidable cost.
AI product and application owners
Operations and support teams
Platform and engineering teams
Risk and governance teams
Business owners accountable for outcomes
Define what matters for the use case and its risk.
Instrument usage, latency, errors, tool calls, integrations, cost, feedback, and relevant quality signals.
Set thresholds, dashboards, alerts, ownership, and response procedures.
Review trends and incidents with evaluation and business outcomes.
Refine monitoring as the solution and risk change.
Usage and adoption monitoring.
Integration and tool failure monitoring.
Latency, availability and cost monitoring.
Safety and policy signal monitoring.
User feedback and escalation monitoring.
Business outcome and task-completion monitoring.
Azure Monitor and related Azure services where appropriate.
Copilot Studio analytics, Azure AI services, and application telemetry.
OpenAI or Claude usage data, IGNA, APIs, databases, ticketing, reporting, and observability platforms.
Security & Governance
Monitoring access, retention, sensitive logs, user privacy, alert ownership, incident handling, and evidence requirements must be defined before production. Monitoring does not replace formal evaluation.
A readiness workshop is the fastest way to find out if this is the right starting point.
