Workflow qualityWithin control
94.6%Target ≥ 92% · 7 days
Agent Operations Observatory
See what accountable AI operation requires.
Inspect quality, human intervention, exceptions, knowledge, cost, policy, incidents, controlled change and rollback across synthetic AI services.
Illustrative environmentAll telemetry, services, incidents, identifiers and results are synthetic. No live client system is connected.
Illustrative service environment
Selected service
Service Recovery Agent
- Version
- v2.4.1
- Owner
- Customer Service Owner
Human interventionWithin control
12.4%Expected 10–18% · 7 days
Exception rateWithin control
3.8%Target ≤ 5% · 7 days
Knowledge freshnessReview
98.2%2 sources due review · 7 days
Median latencyWithin control
1.4sTarget ≤ 2.0s · 7 days
Unit costWithin control
$0.18Budget ≤ $0.24 · 7 days
Policy checksWithin control
100%1,842 / 1,842 passed · 7 days
Service availabilityWithin control
99.92%Target ≥ 99.9% · 7 days
Operating disciplines
Observe more than model health.
- 01Quality
Evaluate business outcomes, actions, answers, exceptions and known limitations.
- 02Control
Monitor authority, policy checks, access, human interventions and residual risk.
- 03Service
Own availability, latency, cost, suppliers, incidents, changes and recovery.
- 04Value
Relate service performance and unit economics to the workflow outcome.
Operate a real capability
Start with the service boundary and accountable owner.
EVVO can help establish monitoring, incidents, change, cost and value routines around a production AI service.
Explore Managed AI Operations