Agentic pipeline system for detecting deviations in AI models and logging compliance

Abstract

An agent-based pipeline system for detecting model deviations in artificial intelligence (AI) and for logging compliance, wherein the system includes the following: a data acquisition unit configured to receive real-time input data and corresponding output data generated by an deployed AI model, wherein the data acquisition unit includes a data synchronization controller structured to standardize and synchronize data streams from different sources; a reference storage unit that is operationally coupled with the data acquisition unit and in which the reference storage unit stores validation metrics and historical performance data of the AI ​​model; a drift detection processing unit connected to the data acquisition unit and the reference storage unit, wherein the drift detection processing unit comprises a computing circuit structured to calculate deviation values ​​between real-time performance metrics of the AI ​​model and baseline validation metrics stored in the reference storage unit; an agent-based control unit communicatively connected to the drift detection processing unit, comprising a decision controller structured to analyze the calculated deviation values, assess regulatory compliance thresholds, and determine corrective actions based on predefined compliance guidelines; an agent control unit operationally connected to a compliance logging unit which has a tamper-proof memory circuit structured to record deviation events, compliance assessment results and corrective actions as time-stamped entries; a rollback control unit connected to the agent control unit and a model version control system, wherein the rollback control unit is configured to restore a previously verified model state stored in the model version control system upon detection of significant deviation events; and a communication interface unit structured in such a way as to connect the data acquisition unit, the drift detection processing unit, the agent control unit, the conformity logging unit and the reset control unit via a secure synchronization bus, thus ensuring deterministic signal propagation.

Description

Technical field of the invention:

[0001] The present invention relates generally to systems for managing artificial intelligence (AI) models and in particular to a hardware-integrated agent-based pipeline system configured for autonomous detection of model drift, dynamic logging of conformity and self-regulating validation of deployed AI models in data processing environments. Background of the invention:

[0002] Artificial intelligence models deployed in production environments are susceptible to model drift due to changing data distributions, contextual variations, and unnoticed environmental influences. Model drift refers to the gradual deterioration of model performance caused by shifts between the statistical properties of the training data and the real-time data. Current model governance frameworks rely on static monitoring dashboards and periodic manual audits, which do not provide continuous monitoring, early anomaly detection, or automated generation of compliance reports.

[0003] Existing drift detection systems largely function as standalone software tools that retrospectively analyze model performance metrics. Such systems cannot be natively integrated into AI pipeline architectures, nor do they possess the autonomous decision-making capabilities to trigger compliance workflows or initiate rollback processes. Furthermore, compliance documentation in regulated industries typically requires manual intervention, leading to delayed audit updates and an increased risk of non-compliance with data protection standards such as GDPR, HIPAA, or ISO 42001.

[0004] Traditional monitoring mechanisms lack adaptive intelligence or the ability to self-regulate, independently diagnose model deviations, and autonomously generate traceable event logs. Furthermore, the absence of integrated hardware-based execution control units limits deterministic behavior, scalability, and the secure anchoring of compliance protocols. Therefore, there is a need for an integrated, agent-based AI pipeline system that autonomously detects model deviations in real time, executes independent compliance workflows, and maintains immutable audit trails across various operating conditions.

[0005] Artificial intelligence (AI) systems have rapidly evolved from research environments to productive use in industries such as healthcare, finance, manufacturing, autonomous systems, and defense. As AI models evolve and operate in dynamic, data-rich environments, they inevitably encounter changes in the distribution of input data, contextual variations, or user behavior. These variations, collectively referred to as "model drift," lead to a gradual degradation of performance, the accumulation of biases, and unreliable predictions. Model drift poses a critical challenge to the integrity, transparency, and reliability of AI systems, especially when these systems are used for decision-making in regulated or safety-critical areas.Therefore, continuous monitoring, validation and verification of the conformity of AI models are essential to ensure that the models used meet expectations and legal and ethical standards.

[0006] The existing technology landscape for monitoring AI models is fragmented and heavily reliant on static architectures. Early AI monitoring tools served as post-implementation analytics dashboards, visualizing performance metrics such as accuracy, precision, hit rate, or performance degradation over time. These systems typically rely on manual evaluation and interpretation of performance trends by human operators, often only after significant degradation has already occurred. For example, while monitoring systems like MLflow, TensorBoard, or Weights & Biases offer visualization interfaces for tracking training metrics and inference performance, they lack the capability for autonomous or agent-based decision-making. They function as passive observers, dependent on developer intervention to detect anomalies or initiate corrective actions.This reliance on manual monitoring leads to delayed detection of model deviations, longer operational interruptions, and potential violations of regulatory provisions.

[0007] A subsequent wave of solutions introduced automated methods for detecting statistical anomalies, integrated into machine learning (MLOps) frameworks. These methods use metrics for comparing distributions, such as the Kullback-Leiffler divergence, the Population Stability Index (PSI), or Kolmogorov-Smirnov tests, to identify statistical anomalies between training and inference data. While these solutions represent an improvement over manual observation, they remain limited by rigid threshold definitions, restricted contextual understanding, and a lack of adaptability. In many practical applications, a data anomaly cannot be directly translated into a performance anomaly, and vice versa, which can lead to false positives or missed detection events.Consequently, these conventional methods for anomaly detection cannot independently distinguish between harmless data fluctuations and critical model degradation that requires intervention.

[0008] In parallel, various AI governance and compliance frameworks have emerged to address regulatory and ethical concerns. Standards such as ISO / IEC 23894 and governance toolkits like IBM's AI Governance Suite and Google's Model Card Toolkit have been developed to ensure the documentation and traceability of activities throughout the model lifecycle. However, these frameworks operate outside the active model inference pipeline and require manual data entry or the retrospective generation of reports. The lack of continuous, real-time integration between model behavior and compliance documentation leads to time gaps that can compromise the reliability of traceability.In industries such as finance or healthcare, where AI-supported decisions have a direct impact on human well-being or financial outcomes, such delays can have serious legal and ethical consequences.

[0009] Recent developments in automated monitoring systems leverage cloud-based MLOps architectures to track model versions, monitor deviations, and manage deployment rollbacks. While these platforms, such as Amazon SageMaker Model Monitor or Azure ML's drift detection services, offer scalability and integration with cloud infrastructures, they often suffer from excessive centralization. Cloud-based systems lead to latency in data synchronization and dependencies on third-party infrastructures, which can be unsuitable for high-security environments or latency-critical industrial control applications. Furthermore, their proprietary nature often limits transparency and makes it difficult for auditors to verify compliance mechanisms or trace data origins.The lack of deterministic control in these systems poses risks for critical sectors such as autonomous driving, defense analytics, and clinical diagnostics.

[0010] Another significant limitation of existing drift detection and compliance monitoring systems lies in their unidirectional architecture. They primarily focus on identifying deviations without a feedback mechanism capable of independently initiating corrective actions such as model rollback, retraining, or generating compliance alerts. In traditional systems, once drift is detected, human engineers must interpret metrics, reconcile performance logs, and manually execute rollback commands. This results in operational inertia, allowing faulty models to continue generating potentially erroneous or non-compliant outputs. Furthermore, current compliance logging solutions typically rely on centralized database entries that can be modified retrospectively, raising concerns about data integrity and tamper resistance.Without immutable, time-stamped compliance records, it becomes virtually impossible to establish responsibility during audits following an incident.

[0011] Attempts have been made to integrate AI monitoring with blockchain-based audit protocols to improve data integrity and traceability. However, such hybrid systems remain largely experimental and lack independent operational capability. They often rely on external triggers to capture events without integrating cognitive control units capable of interpreting anomalies and enforcing policy-based actions. The fragmented nature of these systems leads to inefficiencies and an insufficient level of trust between AI operations and governance frameworks. Furthermore, the computational cost of blockchain transactions makes such systems unsuitable for real-time or high-frequency AI pipelines, where latency and throughput are critical.

[0012] In regulated industries, compliance monitoring requires not only accuracy but also explainability and traceability. However, traditional MLOps tools do not natively integrate explainable AI (XAI) mechanisms into their nonconformity detection workflows. Therefore, even when a nonconformity is detected, system operators cannot readily interpret which characteristics or contextual changes contributed to it. This lack of interpretability hinders effective regulatory audits and undermines trust in AI systems. Furthermore, the absence of continuous validation processes means that compliance assessments are performed only sporadically, leading to extended periods of undetected nonconformity.

[0013] From a systems engineering perspective, most existing drift detection architectures are purely software-based and utilize virtualized computing environments without dedicated hardware control. This architecture leads to limited determinism, increased latency, and vulnerability to external manipulation. Without a hardware-based synchronization and control layer, such systems cannot guarantee real-time performance consistency or protocol immutability. Furthermore, multi-tenant architectures pose additional risks, as they can lead to system-wide disruptions or unauthorized protocol manipulation.

[0014] Another drawback of existing monitoring systems is their lack of integration capability into continuous delivery pipelines while maintaining compliance. As companies adopt agile model deployment methods, AI systems are frequently updated and their parameters optimized. In such dynamic environments, static compliance frameworks fail to capture every change to configurations, hyperparameters, or datasets. The absence of automatic compliance synchronization means that regulatory logs often lag behind the actual model states, resulting in discrepancies between operational status and documentation.

[0015] Recent research on autonomous AI agents has highlighted the potential of self-regulating systems that can learn and act independently within defined operational frameworks. However, the concept of "agentic intelligence" is not yet systematically integrated into the monitoring of AI models. Agentic systems can dynamically assess contextual deviations, analyze policy implications, and independently take corrective action, such as initiating retraining, generating compliance alerts, or activating rollback workflows. The lack of such integrated agentic capabilities in current deviation monitoring architectures leaves a critical gap between detection and regulatory response.

[0016] Despite advances in MLOps platforms, monitoring dashboards, statistical drift detectors, and compliance frameworks, the current state of the art is reactive, fragmented, and heavily reliant on human oversight. None of the existing solutions offers an integrated, autonomous, and hardware-based system capable of simultaneously detecting model drift, enforcing compliance policies, maintaining immutable audit logs, and independently initiating rollback or retraining actions. The absence of such an end-to-end intelligent system leads to operational inefficiencies, increases compliance risk, and compromises the reliability of AI implementations in mission-critical environments.

[0017] The evolution of AI governance requires a new class of system that goes beyond passive monitoring and manually conducted audits. What's needed is an agent-based pipeline that combines hardware determinism with cognitive autonomy—a system that continuously compares real-time outputs with validation metrics, detects and interprets deviations, generates immutable compliance logs, and executes corrective actions without manual intervention. Such a system must integrate agent-based control units capable of interpreting risk thresholds, orchestrating rollback procedures, and ensuring continuous validation in distributed AI environments. By directly integrating decisional intelligence into the operational pipeline, the proposed agent-based system overcomes the latency, fragmentation, and compliance integrity issues of existing solutions.

[0018] The lack of autonomous, self-regulating, and hardware-integrated systems for drift detection and compliance logging represents a significant limitation of the current AI governance infrastructure. The challenges of real-time drift interpretation, immutable compliance documentation, and autonomous remediation of deviations remain unresolved. These shortcomings underscore the urgent need for an agent-based pipeline system for AI model drift detection and compliance logging—a system that integrates intelligent control, secure compliance anchoring, and autonomous corrective workflows into a single, coherent architecture, thereby ensuring operational integrity and regulatory compliance in ever-evolving AI ecosystems. Summary of the invention:

[0019] The present invention describes an agentic pipeline system for detecting model deviations and logging the conformity of AI models. The system comprises several interconnected hardware and software units configured for the continuous monitoring, comparison, and control of AI model performance metrics. It includes a data acquisition unit for capturing input and output streams of operational AI models, a model deviation detection unit that compares real-time outputs with pre-stored validation metrics, a conformity logging unit that records deviations and event triggers, and a rollback control unit that initiates correction or fallback processes upon detecting significant deviations.

[0020] An integrated, agent-based control unit acts as a cognitive feedback processor, autonomously managing policy-based responses, generating compliance documentation, and interacting with the pipeline to enforce validation updates. The system incorporates self-regulating logic that continuously assesses risk signals, generates time-stamped compliance logs, and coordinates rollback procedures in accordance with institutional standards.

[0021] This system is implemented in a physical device consisting of interconnected processors, memory units, and communication interfaces, all integrated into a secure data processing enclosure. The pipeline operates according to defined compliance schemes, supports cryptographically verifiable log entries, and utilizes adaptive methods for analyzing deviations across multiple metrics.

[0022] The main objective of the present invention is to provide an agent-based pipeline system that autonomously detects and minimizes AI model deviations while simultaneously logging regulatory compliance in real time. The invention aims to eliminate the reliance on human intervention in identifying deviations between the performance of deployed AI models and validation metrics. By directly integrating cognitive intelligence into the pipeline architecture, the system continuously monitors real-time outputs, evaluates statistical and contextual deviation parameters, and automatically initiates conformity or corrective actions as soon as deviations exceed defined thresholds. This ensures that deployed models exhibit consistent performance, integrity, and regulatory compliance throughout their entire lifecycle.

[0023] Another important objective of the invention is the establishment of an integrated logging system for regulatory compliance. The invention describes an infrastructure that securely and immutably logs every drift detection event, every validation result, and every model adjustment with a timestamp. The aim is to overcome the weaknesses of conventional compliance systems based on manual data entry and subsequent audits by creating an automated, self-updating log of compliance activities. Through cryptographic anchoring and tamper-proof storage structures, the invention ensures the authenticity and traceability of all logged events, thus enabling transparent and verifiable audit logs that meet the stringent requirements of regulated industries such as healthcare, finance, and the development of autonomous systems.

[0024] Another objective of the invention is the introduction of an agent-based control architecture with autonomous decision-making capabilities within the AI ​​pipeline. The invention comprises an intelligent controller that analyzes drift characteristics, correlates them with defined compliance guidelines, and executes corresponding actions, such as triggering rollback workflows, requesting retraining, or generating risk alerts. This self-regulating behavior enables the system to maintain operational stability and compliance without manual monitoring. The autonomous decision-making framework ensures that each drift event is contextualized and handled according to its severity and impact on the guidelines, thereby reducing false alarms and unnecessary model interventions.

[0025] The invention also aims to provide a hardware-integrated system architecture that ensures deterministic operation, real-time responses, and secure communication between units at all stages of the monitoring process. By integrating processing, storage, and communication units into a secure hardware enclosure, the invention guarantees low-latency drift detection and compliance execution. This structural embodiment overcomes the limitations of cloud-dependent systems, which suffer from synchronization delays, data disclosure risks, and dependencies on third parties. The hardware integration of drift detection and compliance logging further enhances the reliability, scalability, and trustworthiness of the pipeline in mission-critical environments.

[0026] A further objective of the invention is to establish a continuous validation framework that operates in parallel with the AI ​​pipeline to regularly test model outputs against real-time data samples and dynamically update the validation metrics. The continuous validation unit ensures that models are not only monitored for deviations but also actively recalibrated to maintain consistency with the current data distributions. This feature eliminates time gaps between model deployment and conformance assessment and ensures that validation results are constantly updated and verifiable. By linking the validation results with conformance logging, the invention creates a closed feedback loop that guarantees both technical accuracy and regulatory accountability.

[0027] A further objective of the invention is to provide automated rollback and recovery mechanisms that can revert an AI system to its last known stable configuration upon detection of serious deviations or compliance violations. This function minimizes the operational risk associated with the use of faulty models and reduces downtime associated with manual rollback processes. By automatically restoring verified model versions from a secure model version control system, the system ensures uninterrupted operation while maintaining data and compliance integrity.

[0028] The invention also aims to provide a multi-layered framework for risk identification and prioritization that dynamically assesses the severity of model deviation events. Instead of treating all deviations as equal, the system interprets their impact based on context sensitivity, risk exposure, and compliance thresholds defined in the policy repository. The agent-based control unit then classifies drift events into graded categories and triggers appropriate responses based on the assessed risk—from logging the observation to immediate retraction. This structured risk awareness improves the efficiency and precision of compliance management in large AI ecosystems.

[0029] Another key objective of the invention is to bridge the gap between AI monitoring and regulatory audit processes through a fully autonomous compliance documentation system. The proposed system transforms every deviation event, validation test, and model rollback into verifiable compliance documentation accessible to auditors and regulatory authorities in standardized formats. This not only reduces the administrative burden for compliance officers but also ensures that companies are audit-ready at all times – without manual data entry or retrospective documentation.

[0030] A further objective of the invention is to improve the explainability and transparency of AI governance workflows by linking drift detection results with interpretable insights at the feature level. The agent-based control unit can correlate deviations in output behavior with changes in feature distributions or context parameters, thereby generating interpretable explanations for compliance reports. This capability strengthens the confidence of regulatory authorities, enables faster root cause analysis, and increases accountability in decision-making systems where explainability is crucial.

[0031] The invention aims to provide a scalable and interoperable system architecture that can be integrated into various AI pipelines, regardless of the framework or deployment environment. By supporting interoperability with existing MLOps and governance platforms via standardized interfaces, the invention enables companies to integrate autonomous drift detection and compliance logging into their existing infrastructure without extensive reconfiguration. This scalability ensures the efficient operation of the agent pipeline in on-premises, edge, and cloud-based AI deployments. BRIEF DESCRIPTION OF THE IMAGE

[0032] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of an Agentic pipeline system for detecting AI model drift and logging compliance.

[0033] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0034] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0035] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation of it.

[0036] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0037] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0039] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0040] Fig.Figure 1 shows a block diagram of an Agentic pipeline system for detecting drift in AI models and logging compliance. The system 100 comprises: a data acquisition unit (102) configured to receive real-time input data and corresponding output data generated by a deployed AI model, the data acquisition unit containing a data synchronization controller structured to standardize and time-synchronize data streams from various sources; a reference storage unit (104) operationally coupled to the data acquisition unit, in which the reference storage unit stores validation metrics and historical performance data of the AI ​​model;a drift detection processing unit (106) connected to the data acquisition unit and the reference storage unit, comprising a computing circuit structured to calculate deviation values ​​between real-time performance metrics of the AI ​​model and validation metrics stored in the reference storage unit; an agent-based control unit (108) communicatively connected to the drift detection processing unit, the agent-based control unit comprising a decision controller structured to analyze the calculated deviation values, assess regulatory compliance thresholds, and determine corrective actions based on predefined compliance guidelines;a compliance logging unit (110) operationally coupled to the agent control unit, wherein the compliance logging unit comprises a tamper-proof memory circuit structured to record deviation events, compliance assessment results, and corrective actions as time-stamped entries; a rollback control unit (112) connected to the agent control unit and a model version control system, wherein the rollback control unit is configured to restore a previously verified model state stored in the model version control system upon detection of significant deviation events;and a communication interface unit (114) structured to connect the data acquisition unit, the drift detection processing unit, the agent control unit, the conformity logging unit and the reset control unit via a secure synchronization bus, thus ensuring deterministic signal propagation.

[0041] In one embodiment, the drift detection processing unit (106) comprises a floating-point computational processor configured to perform multidimensional metric comparison operations based on accuracy, precision recall, and F1 score parameters derived from the outputs of the AI ​​model.

[0042] In one embodiment, the agent control unit (108) comprises a policy store and a compliance decision processor configured to map deviation events to predefined compliance protocols stored in the policy store.

[0043] In one embodiment, the compliance logging unit includes a cryptographic hash circuit configured to generate a secure hash value for each compliance event log entry for immutable storage verification.

[0044] In one embodiment, the rollback control unit (112) comprises a snapshot retrieval controller configured to retrieve the last stable model version and perform version restoration via a secure transfer protocol below defined rollback thresholds.

[0045] In one embodiment, the data acquisition unit (102) comprises a real-time bus interface controller configured to process heterogeneous input data streams originating from multiple AI models deployed in distributed operating environments.

[0046] In one embodiment, the agent control unit (108) comprises a dynamic risk assessment processor structured to prioritize deviation events based on their statistical significance and the severity of the conformity.

[0047] In one embodiment, the reference storage unit (104) is constructed as a non-volatile solid-state storage medium configured to store basic validation data sets and performance metrics indexed by model identifiers.

[0048] In one embodiment, the compliance logging unit (110) is communicatively connected to an external audit interface which is configured to export structured compliance data sets for regulatory auditing in standardized formats.

[0049] In an embodiment further comprising a continuous validation unit operationally connected to the data acquisition unit and the drift detection processing unit, the continuous validation unit is structured to periodically revalidate the outputs of the AI ​​model using real-time sample datasets to update the basic reference metrics stored in the reference memory.

[0050] The agent-based pipeline system presented here for detecting AI model deviations and logging compliance is a hardware-based, autonomous framework. It continuously monitors the operational reliability of deployed AI models, detects deviations from validation behavior, and automatically initiates compliance and rollback measures as needed. The system is implemented as a computing device consisting of multiple interconnected and synchronously operating units. The overall architecture ensures real-time deviation detection, compliance logging, and model version restoration through intelligent control logic integrated into the hardware processing units.

[0051] In operation, the system begins with the data acquisition unit, which continuously receives streaming data corresponding to both the input and output of one or more deployed AI models. The data acquisition unit includes a real-time bus interface controller that standardizes and aligns incoming data streams through temporal synchronization. Depending on the AI ​​deployment environment, the data can originate from sensor networks, transaction databases, or edge systems. Each incoming data stream is converted into a normalized telemetry format and transferred to both the drift detection unit and the reference memory. The reference memory stores validation metrics derived from the model's training and validation phases, including statistical attributes, performance scores, and feature importance distributions.

[0052] The drift detection unit receives real-time model output metrics and calculates deviation indices relative to the baseline metrics stored in reference memory. It performs statistical comparisons using divergence measures such as the Kullback-Leiffler divergence, the population stability index, or the Jensen-Shannon distance to quantify the degree of distributional deviation between current and reference data. The processing unit includes a floating-point arithmetic circuit that enables high-dimensional metric comparisons with low latency. The calculated deviation vector is then transmitted to the agent control unit, which acts as an autonomous decision center.

[0053] Within the agent-based control unit, a decision controller evaluates the incoming deviation vector against stored compliance policies in a dedicated policy repository. This evaluation process comprises two stages. In the first stage, the controller compares the deviation magnitudes with predefined compliance thresholds to determine whether a deviation exists. In the second stage, it applies context-sensitive risk assessment procedures that consider historical deviation patterns, the stability of the data source, and the operational risk classification. Subsequently, the control unit assigns a dynamic risk rating to the identified deviation event.Depending on the severity, the agent-based control unit initiates one of several actions: Minor deviations may trigger compliance log entries for monitoring, moderate deviations may result in retraining recommendations, and critical deviations initiate a rollback by the rollback control unit.

[0054] When a rollback is triggered, the rollback control unit uses a snapshot retrieval controller to retrieve a verified model version from the model version control system. Each model version includes integrity metadata such as hash codes and performance verification tags. The rollback control unit performs checksum validation using a model integrity checker to ensure that the restored model has not been tampered with. Upon successful verification, the controller initiates the model exchange over the secure internal data bus with minimal downtime. Simultaneously, the compliance logging unit logs the event details, including the type of deviation, the applied compliance policy, and the rollback version identifiers. Each record is cryptographically hashed using a SHA-3-based hash algorithm to generate immutable, timestamped compliance entries.These logs are stored in a tamper-proof, non-volatile storage partition, which authorized auditors can access via a secure communication interface.

[0055] A continuous validation unit operates in parallel with the drift detection unit to continuously evaluate the behavior of the deployed model. It regularly performs validation tests on real-time data subsets and updates the validation metrics stored in the reference memory. The validation results are used by the agent control unit to dynamically recalibrate the conformance thresholds, thus ensuring adaptability to changing data distributions.

[0056] The technique implemented in the drift detection and agent control units follows a deterministic, event-driven control loop. It begins by acquiring new data points, calculating model performance indicators such as prediction accuracy or probability calibration, and comparing them to reference values. After calculating the deviation value, the system applies an adaptive policy mapping that matches the deviation class with stored compliance policies. Each policy entry contains a condition-action rule that determines whether to generate alerts, initiate retraining, or force a rollback. The policy processing circuit executes these mappings in constant time, ensuring a response time of less than one second.A drift detection event thus triggers an atomic compliance update cycle, which includes event logging, risk assessment, and – if necessary – the execution of a rollback. The technique is continued iteratively, forming a self-regulating control loop that ensures continuous operational alignment with compliance standards.

[0057] The components are activated through hardware and firmware integration within a single computer chassis. The data acquisition unit utilizes multi-channel bus controllers with analog-to-digital converters for real-time signal acquisition. The reference memory unit consists of non-transient solid-state storage partitioned into separate data layers for basic metrics and performance protocols. The drift detection unit employs a dedicated arithmetic logic array for floating-point and vector arithmetic. The agent control unit is implemented on a multi-core CPU and combined with a programmable logic device for adaptive inference. The compliance logging unit utilizes tamper-proof flash memory and cryptographic circuitry for secure hash generation and verification.The rollback control unit utilizes high-speed NVMe-based storage controllers for retrieving snapshots and restoring versions. All units communicate via a secure data bus managed by a synchronization controller. This controller regulates timing and ensures deterministic communication between the units. Integrated power management and thermal control systems ensure stable hardware performance during continuous operation.

[0058] The technological advancement of the invention lies in the integration of agent-based intelligence into the hardware-based AI monitoring pipeline. This enables autonomous deviation detection, compliance management, and self-regulating resetting without external control. Unlike existing MLOps-based software monitoring systems, this invention introduces an architecture that integrates compliance analysis, deviation quantification, and the enforcement of corrective actions directly into the hardware control logic. The system combines continuous validation, cryptographic compliance anchoring, and adaptive analysis in a unified framework that can operate autonomously even under regulatory requirements. This achieves a level of determinism, real-time adaptability, and auditability that is not possible with existing cloud-based or purely software-driven systems.

[0059] The technical effect of the invention lies in the real-time detection of deviations in AI models with low latency and autonomous tracking of regulatory compliance. The integrated agent-based control logic ensures immediate risk assessment and initiation of corrective actions, thereby significantly reducing operational downtime and the need for human intervention. Tamper-proof logging of regulatory compliance ensures immutable audit logs, thus strengthening the trustworthiness and accountability of AI operations. Furthermore, hardware-based synchronization and the deterministic communication bus eliminate time delays and guarantee continuous system responsiveness, even with high data throughput.The system improves the reliability, transparency and regulatory compliance of AI implementations in critical sectors and establishes a new paradigm for intelligent and self-regulating AI infrastructures.

[0060] The Agentic Pipeline System for detecting AI model deviations and logging compliance comprises a networked arrangement of data acquisition, analysis processing, compliance management, and autonomous decision control units.

[0061] A data acquisition unit is coupled with multiple AI models deployed in distributed environments and configured to capture inference data, metadata, and feedback signals. This unit includes sensor-driven data stream processors that synchronize heterogeneous data formats and normalize them into structured telemetry data.

[0062] A model deviation detection unit is operationally connected to the data acquisition unit and a reference database. It performs real-time comparisons of the current model's prediction results with the reference metrics, including validation accuracy, F1 score, precision-recall ratio, and feature importance vectors from the model's training and validation phases. If the deviations exceed defined thresholds, a deviation signal is generated and sent to the model's control unit.

[0063] The agent-based control unit comprises a processing architecture with a cognitive decision processor, a policy repository, and a dynamic decision controller. This unit autonomously evaluates the drift signal, determines its regulatory impact, and triggers appropriate operational actions, such as generating risk flags, updating the compliance protocol, or initiating a rollback. The decision controller dynamically compares deviation patterns with predefined compliance criteria and decides whether to perform retraining or a partial rollback.

[0064] The compliance logging unit is communicatively connected to the agent control unit and uses a secure, non-volatile storage medium with cryptographic anchoring. It generates immutable, timestamped log entries for each deviation detection event, including context parameters, deviation magnitude, and corrective actions taken. The log structure is designed for direct compatibility with external audit systems and regulatory validation frameworks.

[0065] A rollback control unit interacts with the operational AI pipeline and, in the event of serious deviations, automatically reverts to a previous stable model version or configuration snapshot. This rollback utilizes model image snapshots stored in the model version repository, enabling rapid recovery without downtime.

[0066] The continuous validation unit operates in parallel with the pipeline, performing periodic inference tests on real-time data segments and updating the validation statistics. The feedback loop between the continuous validation unit and the agent-driven unit ensures adaptive learning and reduces the recurrence of similar drift events.

[0067] All processing units are integrated via a central data bus controlled by a synchronization controller. This controller manages timing and communication between the processes. The system can be deployed as a rack-mounted device or embedded in a cloud-based virtual hardware environment. Isolated storage partitions are available to ensure compliance with regulations.

[0068] Each of the units described above is implemented with standard computer architectures and specialized firmware control. The data acquisition unit uses real-time bus interfaces and ADC controllers to capture data streams. The model drift detection unit uses matrix comparison processors and floating-point computational logic for multidimensional metric analysis. The agent control unit is implemented with multi-core CPUs integrated into programmable logic devices (FPGAs) to perform adaptive policy functions and compliance calculations. The compliance logging unit uses a tamper-proof storage architecture with SHA-3 cryptographic hashing for log authentication. The rollback control unit communicates with model storage controllers over high-speed NVMe connections to perform model version recovery.Communication between the units takes place via an internal data bus, which is controlled by a synchronization controller operating under a secure firmware protocol.

[0069] The proposed system revolutionizes AI-powered compliance monitoring by embedding agent-based intelligence directly into the hardware-integrated pipeline, enabling autonomous decision-making without external control. Unlike traditional passive deviation detection systems, the invention offers real-time compliance anchoring, dynamic rollback, and autonomous documentation. This reduces the need for human oversight and ensures continuous adherence to governance guidelines.

[0070] The system enables real-time detection of model deviations with sub-second latency, ensures immediate updates to compliance data records upon anomaly detection, and guarantees unalterable traceability throughout the entire AI lifecycle. It improves the reliability and transparency of deployed AI systems by introducing an agent-based, self-regulating feedback loop. Furthermore, by integrating deviation detection and compliance mechanisms into the hardware of the operational pipeline, the invention eliminates latency caused by external monitoring software. This enhances system stability, regulatory trustworthiness, and the overall lifecycle control of AI models in production environments.

[0071] The invention relates to the field of infrastructure for artificial intelligence and machine learning, in particular systems and devices for the real-time monitoring, validation, and compliance management of deployed AI models. More specifically, it is an agent-based, hardware-supported pipeline system that detects model deviations, evaluates performance deviations, autonomously manages compliance workflows, and maintains immutable audit logs. The invention lies at the intersection of AI governance, automated quality assurance, and embedded computing systems, and addresses challenges related to regulatory traceability, continuous validation, and operational integrity in adaptive AI environments.

[0072] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0073] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 An agent-based pipeline system for detecting deviations in AI models and logging compliance. 102 Data acquisition unit 104 Reference storage unit 106 Drift detection processing unit 108 Agent Control Unit 110 Compliance Logging Unit 112 Reversing control unit 114 Communication interface unit

Claims

[1] An agent-based pipeline system for detecting model deviations in artificial intelligence (AI) and logging compliance, wherein the system comprises: a data acquisition unit configured to receive real-time input data and corresponding output data generated by an deployed AI model, wherein the data acquisition unit includes a data synchronization controller structured to standardize and synchronize data streams from different sources; a reference storage unit that is operationally coupled with the data acquisition unit and in which the reference storage unit stores validation metrics and historical performance data of the AI ​​model; a drift detection processing unit connected to the data acquisition unit and the reference storage unit, wherein the drift detection processing unit comprises a computing circuit structured to calculate deviation values ​​between real-time performance metrics of the AI ​​model and baseline validation metrics stored in the reference storage unit; an agent-based control unit communicatively connected to the drift detection processing unit, comprising a decision controller structured to analyze the calculated deviation values, assess regulatory compliance thresholds, and determine corrective actions based on predefined compliance guidelines; an agent control unit operationally connected to a compliance logging unit which has a tamper-proof memory circuit structured to record deviation events, compliance assessment results and corrective actions as time-stamped entries; a rollback control unit connected to the agent control unit and a model version control system, wherein the rollback control unit is configured to restore a previously verified model state stored in the model version control system upon detection of significant deviation events; and a communication interface unit structured in such a way as to connect the data acquisition unit, the drift detection processing unit, the agent control unit, the conformity logging unit and the reset control unit via a secure synchronization bus, thus ensuring deterministic signal propagation. [2] System according to claim 1, wherein the drift detection processing unit comprises a floating-point computational processor configured to perform multidimensional metric comparison operations based on accuracy, precision recall and F1 score parameters derived from the outputs of the AI ​​model. [3] System according to claim 1, wherein the agent control unit comprises a policy store and a compliance decision processor configured to map deviation events to predefined compliance protocols stored in the policy store. [4] System according to claim 1, wherein the compliance logging unit comprises a cryptographic hash circuit configured to generate a secure hash value for immutable storage verification for each compliance event log entry. [5] System according to claim 1, wherein the rollback control unit comprises a snapshot retrieval controller configured to retrieve the last stable model version and perform version restoration via a secure transfer protocol below defined rollback thresholds. [6] System according to claim 1, wherein the data acquisition unit comprises a real-time bus interface controller configured to process heterogeneous input streams originating from multiple AI models deployed in distributed operating environments. [7] System according to claim 1, wherein the agent control unit comprises a dynamic risk assessment processor structured to prioritize deviation events based on their statistical significance and the severity of the conformity. [8] System according to claim 1, wherein the reference storage unit is structured as a non-volatile solid-state storage medium configured to store basic validation data sets and performance metrics indexed by model identifiers. [9] System according to claim 1, wherein the compliance logging unit is communicatively connected to an external audit interface configured to export structured compliance data sets for regulatory audits in standardized formats. [10] The system according to claim 1 further comprises a continuous validation unit which is operationally connected to the data acquisition unit and the drift detection processing unit, wherein the continuous validation unit is structured such that it periodically revalidates the outputs of the AI ​​model using real-time sample data sets in order to update the basic reference metrics stored in the reference memory.

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