Runtime signals with operational context
Teams can maintain drift, bias, performance, or other signal types manually or create them through ingestion endpoints. Source, severity, segment, and metric stay traceable.
Product module · Runtime monitoring
Runtime monitoring for AI systems: keep signals, changes, and follow-up work visible in operations
SimpleAct does not stop at pre-deployment documentation. The product keeps monitoring templates, runtime signals, change register, observability profiles, release orchestration, and CAPA actions in one operational context. That shows what changes in production and which measures follow from it.
Visible in the product
Runtime monitoring in SimpleAct is the shift from static documentation to live control. Signals, changes, and evidence stay attached to the system context.
Monitoring templates and runtime signals per AI system
Change register for model, data, and prompt changes
Observability, release orchestration, and CAPA in one operational flow
How SimpleAct handles this
Runtime monitoring connects signals, changes, and governance follow-up work
After go-live, what matters is not how well a form was filled in. What matters is whether teams detect changes in operation and turn them into structured follow-up work. That is the purpose of this module.
Change register and release linkage
Model, dataset, prompt, or deployment changes can be documented with change references, release evidence, and target environment. That creates defensible lineage in operations.
Observability and escalation
Observability profiles bundle metrics, alert thresholds, dashboard URLs, and on-call roles. If a threshold is breached, follow-up work can move into incident management and CAPA.
Product flow
From monitoring signal to operational response
SimpleAct does not keep monitoring as isolated telemetry. It treats it as a work trigger linked to governance, incident management, and evidence.
01
1. Define monitoring and capture signals
Per system, teams define monitoring templates, signal types, metrics, and alert thresholds. Incoming signals are documented with severity, source, and segment.
02
2. Link changes and risks
Signals and changes can feed reassessment, CAPA, or incident management. Teams see whether a model switch, dataset change, or prompt update triggers new obligations.
03
3. Connect external systems
Through API, webhooks, and ingestion endpoints, existing monitoring tools can feed into SimpleAct instead of forcing a second governance layer elsewhere.
What teams can actually control in runtime monitoring
The module is built to translate real production events into traceable governance work.
Monitoring templates per AI system
Runtime signals with severity, metric, segment, and source reference
Change register for model, dataset, prompt, and release changes
Observability profiles with alert thresholds, dashboard URL, and on-call role
Release orchestration with validation suite, pipeline gate, and rollback readiness
Links into incident management and CAPA actions
Frequently asked questions about runtime monitoring in SimpleAct
Is runtime monitoring just a dashboard?
No. Dashboards are only one part. In SimpleAct, the point is to turn signals, changes, and releases into follow-up work and connect them to governance and incident management.
Can I connect external monitoring systems?
Yes. SimpleAct offers API and ingestion endpoints for runtime signals and operational events. Webhooks and enterprise integrations can be used as well.
Why is this relevant for EU AI Act compliance?
Because the risk profile of an AI system is not decided only during initial classification. Changes, drift, bias, or release changes can trigger new obligations and new evidence requirements.
Monitoring only matters when follow-up work reaches the product
SimpleAct connects runtime signals, changes, releases, incidents, and CAPA to the system context. That keeps ongoing AI governance out of disconnected dashboards.
Related topics
