AI-Augmented Dynamic Performance Engineering: A Hybrid Platform Architecture

Abstract

Modern distributed systems encounter unprecedented challenges in maintaining performance stability amid volatile workload patterns and constrained resources. Traditional reactive monitoring methodologies prove insufficient for anticipating degradation and orchestrating preventive interventions. AI-Augmented Dynamic Performance Engineering (AIDPE) presents a hybrid platform architecture synthesizing predictive analytics, autonomous optimization, and intelligent root cause diagnosis. The architecture leverages temporal neural networks for resource demand forecasting, reinforcement learning agents for autonomous parameter adjustment, and transformer-based models for causal incident interpretation. Integration of these components through continuous feedback loops enables anticipation of anomalies, execution of preventive optimizations, and diagnosis of failures with minimal human intervention. Critical gaps in existing methodologies receive direct attention, particularly the inability to correlate predictive signals with optimization actions and fragmentation between detection and remediation systems. Contemporary cloud-native architectures generate massive telemetry volumes, creating an information abundance paradoxically complicating management tasks. The fundamental limitation stems from the absence of closed-loop systems integrating forecasting, optimization, and diagnostic capabilities within unified decision-making frameworks. When prediction models identify impending resource exhaustion, no automated pathway translates insights into concrete infrastructure adjustments. AIDPE addresses these deficiencies through cohesive architectural design, enabling self-adaptive performance management across heterogeneous infrastructure environments.