System for adaptive workflow orchestration based on engineering efficiency metrics

Abstract

System for adaptive workflow orchestration based on engineering efficiency metrics, including: an interface for metric collection that is configured to retrieve real-time performance data from software development and deployment tools; an evaluation engine that is operated to calculate a dynamic efficiency index based on the aforementioned performance data; an orchestration engine configured to adapt active workflows by changing the task order, execution parameters, or control flow in response to changes in the efficiency index; a workflow transformer that is operated to convert workflow specifications between multiple automation formats while preserving logical task dependencies; and a monitoring interface that is operated to display workflow status, performance scores, and customization history for end users.

Description

TECHNICAL AREA OF INVENTION

[0001] The invention relates to workflow management systems and, in particular, systems for orchestrating software development and deployment workflows in real time based on dynamic performance indicators from the field of engineering. It includes systems for continuous integration, delivery, monitoring, and orchestration across heterogeneous computing environments. BACKGROUND OF THE INVENTION

[0002] Modern software development processes consist of several interdependent workflows, including source code management, build automation, testing, deployment, and infrastructure provisioning. These workflows are typically executed in distributed environments using a combination of pipeline engines, monitoring systems, test automation frameworks, and resource planners.

[0003] Traditional workflow automation tools use static configurations that execute predefined sequences of tasks. While these methods offer some consistency in the process flow, they don't account for fluctuating conditions such as unstable tests, infrastructure bottlenecks, code quality degradation, or staffing constraints. As a result, teams often face inefficiencies, pipeline delays, and quality issues that require manual diagnosis and intervention.

[0004] These approaches are typically reactive, relying on the analysis of dashboards and log data. However, these manual approaches are not scalable across different environments or teams and do not respond quickly enough to emerging inefficiencies. Furthermore, many existing systems lack the ability to intelligently adapt workflows across different tools, formats, or platforms—especially in environments with heterogeneous automation stacks.

[0005] Therefore, there is a need for a workflow orchestration system that can capture performance data in real time, dynamically evaluate workflow efficiency, and adjust ongoing processes accordingly. Such a system should be cross-platform compatible and leverage machine learning to predict inefficiencies and proactively provide adaptive solutions. SUMMARY OF THE INVENTION

[0006] The present invention relates to a system and method for real-time, adaptive orchestration of workflows based on engineering efficiency metrics. The system integrates with development and operations tools for capturing performance indicators such as build duration, test success rate, code quality, deployment frequency, and time to problem resolution. These metrics are processed by a central orchestration engine that evaluates workflow efficiency, predicts bottlenecks, and subsequently adjusts the task flow or resource allocation.

[0007] The system consists of five central components: 1. Metrics Collection Interface: A set of connectors or APIs that collects real-time performance data from development, testing, and deployment tools. 2. Efficiency Scoring Engine: A computing module that processes the collected data and generates efficiency indicators for tasks, pipelines, or teams using predefined models and machine learning methods. 3. Adaptive Orchestration Engine: A decision-making instance that compares the calculated efficiency values ​​with benchmarks and dynamically adjusts workflows - by changing the execution order, inserting conditional logic, scaling resources, or disabling problematic steps. 4. Cross-Platform Workflow Transformer: A translation mechanism that converts workflow definitions between different automation formats and pipeline engines, while preserving the execution logic and operational purpose. 5. Monitoring and Oversight Interface: A real-time dashboard and control interface for visualizing metrics, alerts, and adjustments. It allows users to approve, override, or manually adjust automated decisions.

[0008] These components work together to adapt digital workflows to engineering performance data in real time. This increases throughput, minimizes rework, and improves quality assurance throughout the entire software delivery lifecycle. DETAILED DESCRIPTION OF THE INVENTION

[0009] The present invention comprises a modular architecture that enables real-time adaptation of digital workflows in response to performance metrics from the engineering field. The central components of the system include an interface for metric acquisition, an efficiency evaluation engine, an adaptive orchestration engine, a cross-platform workflow transformer, and a monitoring and control interface. Metrics Collection Interface

[0010] The metrics acquisition interface acts as a communication layer between the adaptive orchestration system and the various operational tools used throughout the software development lifecycle. It is designed to support both push and pull data exchange mechanisms, thus ensuring seamless and timely collection of relevant performance data.

[0011] This interface can be integrated with a variety of sources, including version control activity logs, automated build pipelines, test frameworks (for unit, integration and regression tests), code quality analysis engines, bug tracking and incident management systems, and deployment and release automation platforms.

[0012] After capture, the incoming data is standardized and structured by the interface through conversion into a uniform performance schema. This schema includes a consistent set of attributes such as timestamps, task IDs, execution durations, success or error messages, resource utilization, code coverage levels, error rates, and throughput times.

[0013] By standardizing the data, the system ensures that performance signals from a wide variety of tools can be analyzed and interpreted uniformly. Efficiency rating engine

[0014] The efficiency assessment engine continuously processes the standardized data stream to calculate a dynamic efficiency index for each workflow unit—for example, individual tasks, jobs, or pipeline stages. This efficiency index can be determined in various ways, such as through rule-based formulas that weight key performance indicators (KPIs), through time series analysis that compares current values ​​with historical baselines, or through anomaly detection methods that identify statistical deviations. Advanced implementations use supervised learning models trained on labeled performance datasets or reinforcement learning strategies, where adjustment decisions are correlated with improvements in workflow outcomes.

[0015] Each component of a workflow receives a score—typically expressed as a numerical value on a normalized scale (e.g., from 0 to 100)—reflecting its current performance efficiency. If a task's score falls below a defined threshold, the system can automatically initiate corrective or optimization measures. These thresholds can either be set manually by administrators or learned dynamically over time by the system's adaptive components. Adaptive orchestration engine

[0016] The core of the invention is the adaptive orchestration engine, which acts as the central decision-making unit of the system. This engine continuously evaluates the efficiency indices provided by the evaluation engine based on a set of predefined adjustment guidelines. As soon as certain conditions are met—such as a significant drop in efficiency for a task or repeated test failures—the engine can adjust the workflow in real time.

[0017] The adjustments can encompass various strategies: The engine can reorder tasks to resolve dependencies more efficiently, enable or disable optional phases based on recent success rates, allocate more or fewer computing resources to individual job steps, or redirect execution to alternative components with historically better performance. In critical cases, the engine can pause execution and notify operators if permissible fault tolerances are at risk of being exceeded.

[0018] Furthermore, the engine manages a real-time execution graph that maps task dependencies, branching decisions, and current states. It supports versioned rollbacks of customization measures, ensuring complete traceability and operational stability even with dynamic changes. Cross-platform workflow transformer

[0019] To ensure compatibility in heterogeneous environments, the system includes a cross-platform workflow transformer. This module interprets and translates workflow specifications between different configuration formats and automation systems. It is capable of processing domain-specific languages ​​and markup representations such as YAML or JSON and restructuring them to meet the requirements of the target platform.

[0020] The transformer analyzes the logical structure and execution dependencies of the original workflow and converts each element into corresponding constructs of the target system. Operating parameters such as triggers, repetition rules, conditional logic, and concurrency settings are retained or adapted according to the capabilities of the target platform. Transformation metadata is embedded in the new workflow for traceability, reversibility, and auditing purposes.

[0021] This functionality ensures that workflows adapted by the orchestration engine can be reliably deployed across different tools and infrastructures – without the need for manual reconfiguration. Monitoring and control interface

[0022] In addition to the autonomous operation of the orchestration engine, the monitoring and control interface offers human operators complete transparency and control over system behavior. This interface displays the real-time status of ongoing workflows, logs of adjustment decisions, visualizations of performance trends, and alerts regarding inefficiencies or policy violations.

[0023] The monitoring interface features include interactive dashboards with detailed performance indicators (drill-down capable), real-time notification systems with connection to communication channels, and comprehensive audit logs that document the reasoning and result of every system-initiated change.

[0024] Furthermore, the interface includes tools for policy management: Administrators can configure thresholds, define conditional adjustment rules, and approve or restrict specific changes. Manual overrides allow users to interrupt or reverse automated decisions, ensuring that human oversight remains possible at all times, especially in sensitive workflows. Illustrative Example

[0025] Consider a software development team running two deployment pipelines in different execution environments. One of these pipelines has recently been experiencing increased build times and reduced test coverage. The metrics collection interface detects a 30% increase in the average build execution time and a corresponding decrease in test success rates. These metrics are processed by the efficiency rating engine, which assigns low efficiency scores to the affected pipeline stages.

[0026] The adaptive orchestration engine then intervenes and applies a series of corrective actions. It reconfigures the workflow to allow test suites to run in parallel, allocates additional compute resources to the build step, and disables a non-critical documentation generation task that frequently caused errors. The workflow transformer then converts the updated pipeline definition into the format required by the target execution environment.

[0027] All changes are logged and visualized in the monitoring dashboard. System administrators and team leaders receive notifications and can view both the reasoning behind the decisions and the results of the adjustments made.

[0028] Improved build times and higher test success rates can already be observed in the next execution cycle, confirming the effectiveness of the automated measures. Based on these results, administrators can decide to adjust the system's adaptation thresholds or grant the system greater autonomy in similar scenarios in the future.

Claims

[1] System for adaptive workflow orchestration based on engineering efficiency metrics, comprising: an interface for metric collection that is configured to retrieve real-time performance data from software development and deployment tools; an evaluation engine that is operated to calculate a dynamic efficiency index based on the aforementioned performance data; an orchestration engine configured to adapt active workflows by changing the task order, execution parameters, or control flow in response to changes in the efficiency index; a workflow transformer that is operated to convert workflow specifications between multiple automation formats while preserving logical task dependencies; and a monitoring interface that is operated to display workflow status, performance scores, and customization history for end users. [2] System according to claim 1, wherein the evaluation engine comprises a machine learning model trained on historical performance data to predict inefficiencies in the workflow. [3] System according to claim 1, wherein the orchestration engine is further configured to pause or skip workflow phases that fall below a specified efficiency index threshold. [4] System according to claim 1, wherein the workflow transformer performs a semantic mapping of task definitions, trigger conditions and resource configurations. [5] System according to claim 1, wherein the monitoring interface includes policy configuration tools with which conditions can be defined under which workflow adjustments are triggered.