Optimizing Service Delivery Across Enterprise Data Center Infrastructures
Keywords:
Autonomy and control, enterprise data centers, governance, multi-tenancy, service delivery, trust .Abstract
Agentic AI refers to autonomous systems that perceive their environment, reason about it, take actions to influence it, and learn from experience. Enterprise-grade data centers deliver services to multiple users through multimodal workloads running on shared infrastructure, operating as multitenant environments bound by strict service-level agreements and compliance requirements. Agentic AI introduces Level 2 autonomy for optimizing service delivery: control loops close the resulting decision-making boundaries, while policies govern permission models and ensure alignment with enterprise-defined constraints. Existing solutions, however, focus primarily on detecting and responding to anomalies rather than offering fully automated, formally defined agentic systems whose control loops span the entire service delivery stack.
An enterprise-grade data-center ecosystem generates extensive telemetry that characterizes the state of its components and the health of its service deliveries. Monitoring and observability enable the detection of deviations from expected behavior — deviations that require timely action to mitigate performance degradation or security threats, making control loops essential for optimizing service delivery. ML-based detection and prediction models trigger actions such as replacements, scaling, or routing adjustments when needed. Fault-tolerance mechanisms, including replication and checkpointing, allow the system to degrade gracefully during resource pull-outs, while agents continuously monitor system health to ensure these processes remain invisible to end users.
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