Modern-day enterprise-grade computing systems rely on complex integrations across microservices, APIs, and legacy platforms. Integration failures cause a variety of issues, resulting in message loss, resulting in disruption of business processes, along with manual yet complex retry processes, not just limited to retrying the failed transaction from integration, but also sometimes manipulating data in source systems to retrigger the whole transaction. Conventional rule-based monitoring struggles with the volume, velocity, and variability of integration traffic. This paper introduces an AI-powered framework, IntelliFix, for real-time failure detection, root cause isolation, and automated mitigation in integration pipelines. IntelliFix is a cognitively autonomous framework to reengineer integration pipelines as temporal heterogeneous graphs and failure mitigation as a constrained Markov Decision Process (MDP). The core components of the solution include a dual-stage hybrid architecture: • A Temporal Graph Attention Network (TGAT) with payload-aware edge embeddings derived from a domain-adapted BERT encoder for subgraph anomaly forecasting with lead time in minutes. • A Proximal Policy Optimization (PPO) agent augmented with safety-critical action masking and counterfactual regret minimization for reducing Mean Time To Recovery (MTTR). This paper also introduces Diff2Vec, a differential schema embedding technique that captures structural drift in JSON/XML payloads using Siamese contrastive learning.

System Integration Integration failure AI Engineering Machine learning Integration Reliability Predictive Maintenance Deep Reinforcement Learning Graph Neural Networks Federated Learning Schema Evolution