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Modern businesses depend on enterprise integration platforms to connect applications, cloud services, databases, and APIs. When these integration flows experience problems such as high latency, memory issues, connection failures, excessive retries, or increasing error rates, existing monitoring tools generally generate alerts but do not identify the real cause of the problem or automatically resolve it.

As a result, engineers must manually analyze logs, monitoring dashboards, and runtime metrics to determine which part of the integration flow is responsible for the issue. This process is time-consuming, requires specialized expertise, and often results in prolonged outages and service disruptions.

Another limitation is that existing monitoring systems primarily observe infrastructure metrics such as CPU, memory, or network utilization without understanding the structure and execution logic of the integration flows. Consequently, they cannot accurately correlate multiple runtime events into meaningful operational situations or determine the most effective corrective action.

The invention addresses these limitations by providing flow-aware monitoring, intelligent diagnosis, and automated recovery, thereby reducing manual troubleshooting, minimizing downtime, improving system reliability, and enabling continuous operation of enterprise integration environments.

The invention introduces an intelligent runtime management system that represents deployed integration flows as Intermediate Graph Models (IGMs), where nodes represent integration components and edges represent execution paths or interactions. The system continuously collects runtime telemetry such as latency, resource usage, throughput, error rates, queue depth, and retry information and associates this information with the corresponding graph elements.

By analyzing these runtime-annotated graph models, the system identifies undesirable events and correlates them into higher-level operational situations instead of treating each alert independently. Based on the identified situation, the system determines multiple possible corrective actions, evaluates them using predefined criteria such as effectiveness, risk, policy constraints, historical outcomes, and operational cost, and deploys the most appropriate action.

After applying the corrective action, the system continuously monitors the results to verify whether the issue has been resolved. If the selected action does not produce the desired improvement, the system can modify, replace, or revert the action, thereby creating a closed-loop self-healing mechanism. Depending on organizational policies, corrective actions may also require human approval before deployment.

This approach transforms enterprise integration environments from passive monitoring systems into intelligent, self-healing systems capable of automatically detecting, diagnosing, correcting, and validating runtime issues.

Yes.

Existing enterprise monitoring and observability solutions primarily provide infrastructure-level monitoring by reporting metrics, logs, traces, and alerts. Although these tools can detect abnormal behavior, they generally do not understand the structure of enterprise integration flows, cannot correlate multiple runtime events into meaningful operational situations, and cannot automatically determine and execute corrective actions.

The invention addresses this gap by introducing a graph-based representation of integration flows that combines structural information with runtime telemetry. This enables the system to understand how integration components interact, identify higher-level operational situations rather than isolated alerts, and intelligently select corrective actions based on context, policies, risk, and historical effectiveness.

Another significant advancement is the introduction of a closed-loop self-healing mechanism, where deployed corrective actions are continuously validated using subsequent runtime telemetry and automatically refined or reverted when necessary. This transforms monitoring from a passive alerting mechanism into an active, intelligent control system capable of autonomous runtime management.

As a result, the invention significantly advances the state of the art by enabling flow-aware observability, automated diagnosis, intelligent decision-making, and continuous self-healing for enterprise integration platforms.