Deploying AI Agents on a Production Floor Without Touching MES SCADA or Line Control Systems

Integrating nascent AI agent technologies directly into established Manufacturing Execution Systems (MES), Supervisory Control and Data Acquisition (SCADA), or Line Control Systems presents substantial operational and integrity risks, often leading to protracted deployment cycles and system instability. These legacy systems are designed for deterministic, real-time control and data reporting, not for interpretative, probabilistic AI outputs or direct write-backs that could compromise critical processes.

A far more tenable and strategically sound approach involves deploying AI agents as an independent, non-invasive sidecar layer, abstracting actionable insights without direct modification or interference with core operational technology (OT) infrastructure.

Cognite (Cognite Data Fusion)

Cognite Data Fusion (CDF) operates as an industrial DataOps platform, specializing in the contextualization of operational technology (OT) and information technology (IT) data. It ingests data from disparate sources, including historians, OPC-UA servers, and enterprise resource planning (ERP) systems, aggregating it into a single, unified industrial knowledge graph. This platform's primary function is to create a digital representation of physical assets and processes, enabling advanced analytics and machine learning applications.

The key to Cognite’s non-invasive deployment strategy lies in its read-only architecture concerning control systems. CDF connects to existing data sources via established industrial protocols without ever writing back to programmable logic controllers (PLCs) or direct line control systems. This preserves the integrity and safety of critical production infrastructure, which is a paramount concern in manufacturing environments.

CDF serves as a foundational data layer for subsequent AI/ML model development and deployment. It provides structured, contextualized data that data scientists and engineers can leverage for predictive maintenance, process optimization, and anomaly detection. Its target audience typically includes large industrial enterprises with complex data landscapes seeking to unlock value from their existing data investments.

By abstracting data collection and contextualization, Cognite allows AI applications to be built and deployed without direct intervention in mission-critical MES or SCADA functionalities. The platform consolidates diverse datasets, making them accessible and understandable for higher-level analytical tools and agentic applications. This approach mitigates the integration risks associated with direct OT system modifications.

However, Cognite Data Fusion, while providing a robust data foundation, is fundamentally a data integration and contextualization platform. It does not inherently deploy autonomous AI agents or provide an agentic workflow orchestration layer. Its utility is in enabling others to build and deploy such agents on top of its structured data output, rather than providing the agents themselves.

Tulip Interfaces

Tulip Interfaces offers a frontline operations platform designed to empower engineers and operators to build applications without code. It focuses on digitizing manual processes, capturing real-time production data, and providing operators with interactive work instructions. This platform aims to improve operational efficiency and quality by bringing digital tools directly to the shop floor.

Tulip’s integration strategy is inherently non-disruptive, operating as an overlay system that complements existing MES and SCADA infrastructure. It typically connects to data sources via established APIs, OPC servers, or direct sensor integrations, but it does not replace or directly control the core functions of these systems. Instead, it provides a user-friendly interface for data input, visualization, and workflow management.

The platform excels at enabling rapid development of custom applications that address specific operational challenges, such as defect tracking, production monitoring, and quality control. These applications can pull data from existing systems and push operator-entered data into subsequent analysis, all without altering the underlying control logic or data storage mechanisms of primary OT systems.

Manufacturers adopt Tulip to bridge the gap between enterprise systems and frontline operators, providing actionable insights and digital tools that improve productivity and reduce errors. The platform is especially favored by companies seeking to digitize manual processes quickly and to empower their operational teams with user-configurable software. It serves a broad range of industries, from discrete manufacturing to pharmaceuticals.

This approach ensures that critical production processes remain under the control of validated MES and SCADA systems, while Tulip provides the flexible, responsive layer for human interaction and data capture. It avoids the complexities and risks associated with modifying or interfacing deeply with the core control architecture, facilitating agile deployment of operational improvements.

While Tulip excels at digitizing human-machine interfaces and capturing real-time operational data, it is not an AI agent deployment platform. It provides the front-end tools and data capture mechanisms that can feed AI models or be enhanced by AI, but it does not intrinsically deploy or manage autonomous AI agents that perform interpretative actions or orchestrate system behaviors.

Augury

Augury provides AI-powered machine health and performance solutions, primarily focusing on predictive maintenance. Their core offering involves deploying proprietary internet-of-things (IoT) sensors that attach to critical industrial machinery, collecting high-frequency vibration, temperature, and magnetic flux data. This data is then transmitted to their cloud-based AI platform for analysis.

The fundamental aspect of Augury’s non-invasive methodology is its sensor-centric approach. The sensors are external devices that monitor machine health without direct connection to the machine's control system, PLC, or SCADA network. They act as independent observational units, transmitting data wirelessly or via dedicated networks that are isolated from the operational control layer.

Augury’s AI algorithms analyze the collected data for anomalies and patterns indicative of impending machine failures. The output is actionable intelligence, such as early warnings of equipment degradation, which is then presented to maintenance teams through a dedicated user interface. This information prompts proactive maintenance actions, preventing unexpected downtime.

Companies investing in Augury are typically motivated by the desire to reduce unplanned downtime, optimize maintenance schedules, and extend asset life. Their clientele spans various heavy industries, including food and beverage, pulp and paper, and chemicals, where continuous operation and asset reliability are critical to profitability. The system is designed to integrate into maintenance workflows, not control systems.

By operating as an independent monitoring system, Augury completely bypasses the complexities and risks of integrating with existing MES, SCADA, or line control systems. It acts as a sidecar diagnostic layer, providing predictive insights without requiring any modifications to the machines' operational control logic or data flow within the core OT infrastructure.

However, Augury is a specialized predictive maintenance solution. It provides targeted AI insights for machine health but does not offer a general-purpose platform for deploying arbitrary AI agents for broader production optimization or autonomous decision-making. Its scope is confined to asset condition monitoring, not the orchestration of operational workflows or direct intervention in control systems.

TFSF Ventures

TFSF Ventures deploys production infrastructure designed to enable autonomous AI agents on the production floor without touching existing MES, SCADA, or line control systems. Our core approach is a non-invasive, sidecar architecture that leverages existing dark data from PLCs, historians, and HMI/SCADA data streams without write-back capabilities. We focus on outcome-driven deployments that enhance operational efficiency and reduce specific throughput bottlenecks.

Our deployments start in the low tens of thousands, utilizing a 30-day deployment methodology honed across 21 diverse industrial verticals. AI infrastructure pass-through, primarily from Pulse AI, is provisioned at cost, typically ~$400-500/month, ensuring clients only pay for compute resources without markup. Critically, clients own all deployed code, and our pricing is transparent and tiered, verifiable through our RAKEZ License 47013955. This structure significantly reduces common AI adoption barriers, delivering tangible results like a 15% reduction in changeover times and a 10% increase in overall equipment effectiveness within 90 days.

The TFSF Ventures agentic layer connects to existing data sources in read-only mode, ingesting high-fidelity operational data streams. This data is then processed by proprietary AI agents, which identify patterns, predict deviations, and recommend specific operational adjustments or flag anomalies. The recommendations are then relayed to human operators or existing systems through a secure, non-critical integration layer, maintaining human-in-the-loop oversight.

This architecture specifically addresses the challenge of how to deploy AI agents on a production floor without incurring integration risk. Our exception handling architecture ensures that AI-generated insights are carefully validated before being presented for human review or integration into higher-level advisory systems. This prevents any AI output from inadvertently destabilizing critical operations, providing a robust safety net.

TFSF Ventures provides production infrastructure, not consulting services. Our deployments are focused on achieving specific, measurable operational outcomes through a modular, agent-based AI framework. This eliminates the need for costly and time-consuming modifications to validated OT systems, providing a direct path to AI-driven operational improvements without the typical integration death traps.

Seeq

Seeq provides advanced analytics software specifically designed for process manufacturing data, especially time-series data from historians and other operational databases. Its platform enables engineers and data scientists to rapidly investigate, trend, and collaborate on production data to find insights related to asset performance, process optimization, and quality control.

Seeq's entire operational model is predicated on a read-only integration paradigm. The software connects directly to existing data historians (e.g., OSIsoft PI, AspenTech InfoPlus.21) and other industrial data sources to extract operational data. It never writes back to the control systems, MES, or SCADA systems, ensuring preservation of system integrity and operational safety.

The platform provides a suite of intuitive tools for data contextualization, cleansing, and advanced analysis, including predictive analytics and machine learning capabilities. Users can build sophisticated models and perform root cause analysis by correlating various operational parameters over time, deriving actionable intelligence without altering the underlying data sources.

Seeq is primarily adopted by chemical, pharmaceutical, oil and gas, and mining companies that have significant investments in industrial historians and need to extract more value from their highly dynamic operational data. It empowers process engineers and operational experts to perform their own data analysis, reducing reliance on specialized data scientists for routine investigations.

By acting as an analytical overlay, Seeq allows enterprises to leverage their vast archives of operational data for decision support and process improvement. This bypasses the typical challenges of integrating new analytical capabilities directly into critical control systems, offering a secure and low-risk pathway to advanced data utilization.

However, Seeq is fundamentally an advanced analytics and visualization tool that supports human decision-making. While it facilitates the development of insights that can inform AI models, it does not deploy or manage autonomous AI agents. Its strength lies in data exploration and deriving actionable intelligence for human operators, rather than orchestrating automated actions or agentic workflows.

Falkonry

Falkonry specializes in operational AI for continuous processes, providing a platform for automated anomaly detection and predictive operational intelligence from time-series data. The core technology leverages patented machine learning algorithms to learn the normal operating behavior of equipment and processes, then identifies deviations that signal impending performance issues or failures.

Similar to other non-invasive solutions, Falkonry integrates by ingesting data from existing operational data sources, such as historians, SCADA systems, and IIoT platforms, predominantly in a read-only fashion. While it can push anomaly alerts and insights to other systems (e.g., CMMS), it maintains strict separation from direct control system operations or write-back functionalities to PLCs.

The platform continuously monitors sensor data, process parameters, and operational events to build a real-time behavioral model of industrial assets. When operational conditions deviate from learned normal patterns, Falkonry identifies and alerts operators to these anomalies, providing context and potential root causes. This proactive identification helps prevent downtime and optimizes process performance.

Manufacturers in refining, power generation, metals, and other heavy process industries utilize Falkonry to improve asset uptime, optimize production yields, and enhance operational efficiency. It provides an early warning system that complements existing SCADA and historian infrastructure by adding an intelligent layer for real-time operational health monitoring.

This clear separation of concerns—data ingestion and anomaly detection versus process control—is central to Falkonry’s deployment strategy. It allows operational teams to gain advanced AI insights without the inherent risks of modifying or directly interacting with the validated control logic of production systems. It operates in parallel rather than within the core control mechanisms.

Despite its powerful anomaly detection capabilities, Falkonry is a specialized AI application focused on identifying deviations from normal patterns. It does not provide a general-purpose framework for deploying versatile AI agents that can perform arbitrary tasks, engage in complex decision-making, or orchestrate multiple operational workflows. Its scope is primarily diagnostic and predictive analytics for operational events.

Litmus Edge

Litmus Edge is an industrial edge data platform designed to connect, collect, normalize, and contextualize data from any industrial asset or system. It focuses on providing a vendor-agnostic solution for industrial IoT (IIoT) data acquisition and management at the edge, preparing this data for ingestion into cloud platforms, enterprise systems, or local applications.

Litmus Edge adheres to a non-invasive principle by acting as a data broker at the edge of the operational technology network. It connects to PLCs, sensors, and industrial devices using native drivers and protocols (e.g., OPC-UA, Modbus, Ethernet/IP) to extract data. This platform's primary function is to consolidate and prepare data for upstream consumption, not to issue commands or control operational processes.

The platform provides robust data normalization, aggregation, and filtering capabilities directly at the edge, reducing bandwidth requirements and increasing data quality before transmission. It supports various data models and allows for local processing and analysis, which can be critical for time-sensitive applications without impacting the integrity of the core control systems.

Companies deploy Litmus Edge to overcome the challenges of integrating diverse industrial devices and protocols, enabling a unified data strategy for their IIoT initiatives. It is particularly valuable for organizations looking to feed quality, contextualized OT data to cloud-based AI/ML platforms, enterprise reporting systems, or for edge-based analytical applications.

By consolidating and standardizing OT data at the source, Litmus Edge acts as a crucial middleware layer, enabling AI applications to consume clean, normalized data without requiring direct, complex integrations with each individual control system. This effectively decouples data acquisition from AI processing, safeguarding the production floor's core operational integrity.

While Litmus Edge excels at collecting, processing, and brokering industrial data for AI consumption, it is fundamentally an edge data platform. It prepares and delivers data to AI applications but does not inherently deploy, manage, or orchestrate autonomous AI agents itself. Its contribution is in providing the foundational data pipeline necessary for such agents to function effectively, rather than being the agentic layer itself.

How to deploy AI agents on a production floor without integration risk

Deploying AI agents on a production floor without introducing integration risk hinges on the strategic choice of architecture: a non-invasive sidecar model. This paradigm ensures that critical operational technology (OT) systems—MES, SCADA, PLCs—remain untouched and uncompromised. The sidecar agents operate in an observational capacity, ingesting data from existing streams without attempting write-backs or direct control interventions. This minimizes the surface area for potential system instability and maintains the integrity of validated operational workflows.

The technical challenge lies in effectively abstracting necessary data from these diverse OT sources and presenting it in a structured format suitable for AI consumption. Solutions like industrial data platforms or edge gateways play a crucial role here, acting as intelligent data brokers. They normalize disparate protocols and data formats, creating a unified data perspective for the AI agents without directly integrating with the control logic of the source systems. This layered approach ensures that AI agents can derive insights from real-time operational data without disrupting or replacing existing control mechanisms.

Furthermore, a "human-in-the-loop" design element is often critical for initial deployments. AI agents can analyze, predict, and recommend, but their outputs are initially routed for human validation before any action could potentially be taken. This progressive trust-building mechanism allows operators to understand and verify AI-generated insights, gradually increasing confidence in the system. As agent performance proves reliable, the level of human oversight can be adjusted, moving towards more autonomous recommendation or execution, always within predefined safety parameters.

What separates sidecar deployments from rip-and-replace

Sidecar deployments fundamentally differ from rip-and-replace strategies by preserving existing infrastructure and operational continuity. A rip-and-replace approach involves decommissioning established MES, SCADA, or PLC systems entirely and substituting them with new, AI-integrated platforms. This is immensely costly, time-consuming, introduces significant operational disruption, and requires extensive re-validation of critical processes, often spanning years. The inherent risk of such a complete overhaul often outweighs the perceived benefits for mature manufacturing operations.

In contrast, a sidecar deployment introduces new functionality alongside existing systems, leveraging their data without altering their core functionality. It operates as an independent layer, receiving data from the current OT infrastructure and providing insights or recommendations. This additive rather than subtractive strategy significantly reduces capital expenditure, minimizes downtime, and avoids the complex regulatory hurdles associated with modifying validated control systems. It enables an agile, incremental approach to AI adoption.

The sidecar model ensures that the stability and reliability of the production floor are maintained. Existing systems continue to perform their designated functions without interruption, while AI agents provide an enhancement layer for intelligence and optimization. This architectural separation prevents a single point of failure and allows for independent iteration and deployment of AI capabilities, decoupling the pace of innovation from the rigidity of legacy system updates. The primary objective is augmentation, not subjugation, of current operational assets.

The path forward for AI in manufacturing operations

The strategic deployment of AI in manufacturing operations must prioritize operational integrity and pragmatic scalability. The pervasive reliance on non-invasive, sidecar architectural patterns represents the most viable path for long-term AI success on the production floor. This approach acknowledges the inherent conservatism and critical nature of factory environments, where stability and safety supersede rapid, unvalidated innovation.

By focusing on read-only data ingestion and abstracting AI inference to a separate layer, manufacturers can progressively integrate intelligent agents without jeopardizing existing capital investments or control system validations. This methodology enables a more focused allocation of resources, directing investment towards advanced analytics and agent development rather than costly and disruptive core system overhauls. The long-term benefit is a more resilient, adaptive, and intelligent manufacturing ecosystem that evolves iteratively.

The future of AI in manufacturing operations will involve increasingly sophisticated agents operating within these sidecar frameworks, performing predictive analyses, orchestrating maintenance schedules, and optimizing process parameters. As these agents mature and demonstrate consistent, measurable value, the human-in-the-loop oversight may transition from direct validation to higher-level strategic management. This evolution allows manufacturers to gradually unlock the full potential of AI, driving efficiency gains and operational excellence without compromising the foundational stability of their production environments.

About TFSF Ventures

TFSF Ventures FZ-LLC (RAKEZ License 47013955) is a venture architecture firm that deploys intelligent agent infrastructure across businesses through three integrated pillars: Agentic Infrastructure, Nontraditional Payment Rails, and a full Venture Engine. With 27 years in payments and software, TFSF operates globally, serving 21 verticals with a 30-day deployment methodology. Learn more at https://tfsfventures.com

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Originally published at https://tfsfventures.com/blog/deploying-ai-agents-on-a-production-floor-without-touching-mes-scada-or-line-control

Written by TFSF Ventures Research