Operate after launch
Managed AI and automation operations
Monitor quality, incidents, cost, integrations, and change across production AI and automation workflows with accountable ownership.
Operating problem
Model, data, prompts, APIs, and business rules all change after launch. Without an operating owner, pilot quality decays and failures are difficult to reproduce.
Useful first outcomes
- Defined service ownership
- Quality and cost monitoring
- Incident and change runbooks
- Scheduled evaluation and improvement
Delivery pattern
- 1Establish service inventory and operating objectives
- 2Instrument traces, quality, latency, and cost
- 3Define incident, escalation, and rollback procedures
- 4Review changes and outcomes on a regular cadence
Controls built into scope
- Least-privilege operational access
- Auditable releases and rollback
- Sensitive-data-aware telemetry
- Human approval for material capability changes
Services that support this use case
Managed AI & Automation Operations
Operate AI agents and automations after launch with monitoring, incident response, quality reviews, cost controls, change management, and continuous improvement.
Explore serviceAI Evaluation & Observability
Measure groundedness, task completion, tool use, safety, latency, and cost before release, then trace and monitor quality in production.
Explore serviceCloud & Data Architecture Review
Review cloud platforms, data pipelines, quality, security, cost drivers, and operating risks, then define a practical modernization roadmap.
Explore service