Operational monitoring
Track failures, latency, usage and cost. Establish actionable alert thresholds and a clear route to the person responsible for investigation.
Keep AI systems useful as knowledge, integrations, providers and business requirements change. With explicit ownership and an agreed service scope.
Discuss managed operationsThe starting question
Availability alone is not enough. A knowledge index can become stale, a provider update can change behaviour and a workflow can drift away from current business practice. Managed operation combines technical monitoring with evaluation of the work the system is supposed to do.
An operating baseline, monitoring and evaluation plan, service responsibilities, a maintenance schedule and a recurring improvement review.
The work / From design to operation
Track failures, latency, usage and cost. Establish actionable alert thresholds and a clear route to the person responsible for investigation.
Run representative examples and review material changes in behaviour. Separate poor source information, retrieval problems and generation errors so improvements address the cause.
Maintain integrations, dependencies and knowledge refresh processes. Test changes, record the release and preserve a practical rollback path.
Review incidents, corrections, user feedback and operating costs. Prioritise the next improvements against the workflow’s actual value and criticality.
Designed into the system
Agreed service hours and responsibilities
Incident escalation and recovery procedures
Quality checks around changes
Transparent operating and provider costs
Only when explicitly included in the service agreement. Support hours, response expectations and system criticality must be aligned before the service begins.
Subject to an assessment of the code, access, documentation, dependencies and current risks. The initial work may include establishing missing monitoring, tests or operational procedures.