Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- Agentic AI holds the key to unlocking transformative changes in automotive manufacturing information technology.
- Building synergy between GenAI, Agentic AI and Retrieval Augmented Generation (RAG) will have significant practical applications for the automotive sector.
- SAAS is at the cusp of being replaced by Agentic AI. [Ref1] [Ref2] For the automotive sector, this impacts in-house HR, finance, accounting, quality management applications to Advanced Driver-Assistance Systems (ADAS) and conveyor material handling.
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Why Agentic AI?
The automotive manufacturing industry is at a pivotal juncture, driven by rapid digital transformation.
From machine learning models to AI-powered systems, industrial operations have evolved tremendously. Yet, many production processes still depend on human oversight, making them prone to inefficiencies and delays. While technologies such as GenAI and AI agents are being deployed to address this , many challenges still remain.
Agentic AI is a framework that combines new forms of artificial intelligence (AI) like large language models (LLMs), traditional AI such as machine learning, and enterprise automation tools to create autonomous AI agents that can analyze data, set goals, and take actions with decreasing human supervision. Agentic AI helps to move AI from Asssitive to Agentic.
Agentic AI can :
- Accelerate the product development for OEMs by validating multiple design alternatives.
- Enhance the Smart Manufacturing in Industry 4.0 taking factories to higher efficiency, lower downtime and better resource utilization.
- Transform in-vehicle intelligence with next-gen autonomous driving systems.
- Personalize Customer experience with AI agents learning driver prefences and suggest routes, entertainment and maintenance.
- Improved Sustainability & Energy Optimization. AI Agents can optimize energy usage in battery with charging strategies, hence, reducing the carbon footprint.
Agents autonomously makes decisions, adapts to changing environment and continuously optimizes strategies, freeing organizations from rigid rule-based ML or static AI systems enabling organizations for faster & more intelligent responsiveness.
Agentic AI is a tremendous advancement, where AI agents go beyond prediction and recommendation. These intelligent systems take autonomous actions, adapt in real time, and optimize production flow without the need for constant human intervention. With the power to analyze real-time data, learn from it, and make decisions on the go, Agentic AI marks the next big leap in industrial automation.
In this white paper, we delve deep into the role of agentic AI systems in transforming automotive manufacturing operations, enhancing production efficiency, improving quality control, and driving supply chain optimization.
Flavors of AI
AI agents are task-oriented, rule-based, have limited autonomy, performing specific actions based on pre-defined rules.
For automotive manufacturing, AI agents could be used for specific tasks like quality control on a specific part of the line or for customer service interactions or material handling.
Agentic AI is suited for complex, multi-step processes; exhibits a higher degree of autonomy; and is strategic; allowing it to reason, plan, and be adaptive, flexible to changing circumstances to achieve broader goals. For automotive manufacturing, Agentic AI could be used to optimize an entire assembly line, dynamically adjusting production based on real-time data and potential disruptions.
Agentic AI has emerged with rapid development in Large Language Models (LLMs), where agents are used as a tool for ’thinking’. ‘Thinking’ refers to the grasp of the sequence of tasks required towards achieving a given goal. Whereas GenAI generates content – code, text, video, audio based on prompt entered – Agentic AI has the capability to sense the physical or virtual environment (with the cascading effect of GenAI) and communicate with other agents and act toward autonomous decision-making and task execution. GenAI generates agent to agent communication and has machine vision to sense an environment and generate a final response. GenAI is primarily for creating, while Agentic AI focuses on executing.
In the future, automotive manufacturing is likely to see a combination of both, with Agentic AI systems coordinating and managing the overall production process, while AI agents handle specific, well-defined tasks within that system.
Use Cases
There are two types of Agentic AI: virtual Agents that are goal based, act indepenedently, and make dynamic decisions; and physical agents that are rule based, act predictably, and take deterministic decisions.
Virtual agents perform tasks like providing customer support through text or voice, managing email or generating automated responses. A physical agent is an AI system integrated into a physical robot or system that interacts with and acts in the real world, like autonomous vehicles or a robot in a factory or autonomous vaccum cleaner. Physical agents interact with the physical world through sensors.
Use Cases of Agentic AI in Automotive Industry
Adaptive Regulatory Compliance with Agentic AI: Manufacturers face complex compliance requirements across local, regional, and global regulations enforced by bodies like the National Highway Traffic Safety Administartion(NHTSA) for vehicle safety and the Environmental Protection Agency(EPA) for emissions standards. Tracking evolving laws, managing diverse reporting formats, and ensuring timely submissions demand significant resources. Agentic AI can address this challenge by autonomously monitoring regulatory changes, interpreting requirements, and orchestrating end-to-end compliance workflows—from data collection to reporting—reducing cost, effort, and risk of non-compliance.
Intelligent quality control: By integrating vision analytics and multisensory data, Agentic AI can monitor product quality across the production line with a high degree of accuracy. It not only identifies anomalies like surface defects (seat wrinkles in car seats, instrumentation panel anomaly detection or dimensional deviations) but also takes corrective actions by adjusting relevant process parameters upstream or downstream. This capability significantly reduces the volume of defective products and minimizes rework. The AI system continuously learns from new patterns of defects to refine its detection models, ensuring ongoing improvement in quality assurance. With earlier traditional ML models used in quality control Machine Vision, analyzing data usage to be separate individual models, requiring intensive training with minimal adaptation. Agentic has emerged as a solution for this.
Closed-loop process optimization: Every manufacturer has quality management standards depending on the region and industry of operation for the part(s). There is vast documentation for these quality standard procedures (e.g. ISO-9000, IATF-16949, TQM, Six Sigma and so on) to sync with the data coming from the metrology department. AI agents can be efficient in analyzing the data and suggesting procedures to meet quality standards based on documentation provided by the manufacturer. This AI is trained on standard documentation shared by manufacturers to do repetitive tasks and guide a quality inspector to make faster decisions.
Supply chain resilience: Agentic AI can manage procurement and logistics by sensing disruptions such as supplier delays or transport issues and autonomously find alternatives like routing deliveries, adjusting order volumes, or negotiating timelines with vendors, thereby optimizing supply chain management. Solving Supply chain problems has been eminent with every technological advancement. -However, real-time analysis and decision making with different predictions like weather and political cases was not achievable. Agents are solving the supply chain problems rapidly.
Material handling: At warehouses, logistic operation centers and robotic assembly lines, leveraging Agentic AI can optimize the processes. Agents can interface with suppliers and transportation systems to achieve adaptive and strategic material planning and ordering. Agents can handle material over conveyors to segregate and assemble parts. This helps to achieve just-in-time inventory management at manufacturing plants. In Robotics automation, handling exceptions was heavily dependent on base code. Any new exception would need humans to be occupied with problem solving. However, with agentic - handling exceptions, it is decided quickly on a wide variety of data available on internet (with LLM) and providing instant solution with maintaining synergy with other robots in function.
Finance, accounting and human resources: Agentic AI has the capability to invoice, report finances, manage payroll accounting, as well as detect anomalies in financial data. It can also conduct seamless recruitment, predict job performance and provide HR support. Agents provide human-like experience that was never there in traditional AI systems.
Advanced driver assistance system: Agentic AI integrates real-time data from cameras, LiDAR, and V2X (vehicle-to-everything) networks to dynamically predict risks, reroute around traffic bottlenecks, and adapt driving behavior to road conditions. Unlike rule-based ADAS, these systems learn from edge cases (sudden pedestrian crossings) and collaborate with other agents (smart traffic lights) to optimize safety and efficiency. By autonomously balancing driver preferences, traffic laws, and environmental factors, Agentic AI transforms ADAS into a self-optimizing co-pilot, accelerating the shift toward fully autonomous mobility.
Reference architecture
Agentic AI has three basic requirements – brain, tools, and target.
Agentic AI Architecture
The ‘brain’ is an LLM that understands the user prompt, decides on the sequence of actions, and generates a required response for user. To take action, agents need access to the tools which are necessary to perform a specific action. for instance, an SAP Consultant agent needs access to the tables/modules in SAP to perform the action. To understand when and what action is to be taken it needs access to the requisite information. To perform the actions LLM needs to be grounded with concise objectives or ‘targets’ since LLMs are defined with billions of parameters/nodes with all available data and specific boundaries need to be set.
For actions to be performed, in practice multiple agents defined with specific tasks that collaborate with each other towards achieving a larger goal are required, with a supervisor agent to orchestrate.
Based on the specific use case, the architectural building blocks of Agentic AI vary. Selection of Agai LLM, number of tasks and so individual agents required in orchestration will change and hence the respective manufacturing enterprise architecture decisions tree.
Agentic AI - Automotive Manufacturing Reference architectural Layers
Digital platforms, utility building blocks, cloud, and external apps will be the standard building blocks of the architecture. The manufacturing stakeholders, which include customers, dealers, distributors, and partners, sit at the top of the architecture. This is followed by the user interface (UI) and interaction layer, services agent orchestration layer, and business workflow layer which already exist within an enterprise.
Agent feedback and improvement, agentic model layer, and data and memory layer processing are the key layers related to Agentic AI. The agent feedback and improvement layer is part of learning for an agent. As AI actions are not yet entirely reliable, businesses need humans to respond in the loop. Based on the human response, there is reward and regularization in learning for the next repetition of the task. Below this is the brain of the agent i.e. the agentic model layer. It is a fine tuned or industry-specific LLM. Underlying it is the data and memory layer that has data files in format of text, video, audio or code specific to a business.
Responsible AI is key to trust in agentic technology. Human access to check logs that verify the processes of the agent’s decision allows for necessary modifications that error-proof agentic decisions.
Risks and mitigation
While Agentic AI offers several benefits, the automotive industry also needs to be cognizant of the risks and challenges.
The most common risks are data security, biases of the system, and trust in the outcome. All of these can have adverse and serious consequences. Some of the additional risks are: data privacy, job displacement, regulatory ambiguity, agent-to-agent miscommunication leading to financial fraud and so on.
Data privacy: As we provide access to enterprise tools, databases, applications or code repository to LLMs, they have control over actions. This raises an alarm as any errors in the prompt or the process, or the hallucination of an LLM can have real and serious business impacts.
Intellectual property (IP): Manufacturers will have to discover how they can protect their IPs by using Agentic AI. Copyright or IP breaches from third parties will be a bigger cause of concern, as GenAI models can source information from a vast pool of data over the internet.
Explainability (interpretability): Many existing AI systems are not designed for explainability, and they are sometimes opaque in their decision-making processes. It is important to develop explainable AI systems that provide clear and transparent explanations for their decision-making processes, to build trust and confidence.
The solution that many tech giants are coming up to address this is Retrieval Augumented Generation(RAG). The RAG is an AI framework that grounds or fixes the AI with artifacts provided by us for an agent to only respond within contexual boundaries. This offers more trust and reliability on the content or decisions taken by an agent as the rules are determined by the business.
Data security can be addressed by a Human-in-the-Loop (HITL) decision approach. AI agents are designed to autonomously handle routine security tasks and threat detection, while humans are experts in context-aware decision-making. HITL at critical junctures enhances the robustness and reliability of AI-driven security framework.
Road ahead
The strategic adoption of Agentic AI represents more than just a technological upgrade—it's a key milestone for automotive competitiveness.
Agentic AI will play a pivotal role in transforming a connected, collaborative, and cognitive ecosystem of automotive manufacturers. These intelligent systems autonomously orchestrate complex manufacturing workflows, negotiate supply chain dynamics in real-time, and enable mass personalization at scale. Agentic technology will fundamentally rewrite the next horizon of innovation in vehicle production.
Automakers that embrace this new innovative technology are taking a crucial step toward reducing manufacturing time, building smarter and more sustainable manufacturing operations and improving their go-to-market process, which will help them gain an edge in the competitive landscape. By harnessing Agentic AI in a smart and ethical way, automakers can redefine manufacturing excellence and drive the future of mobility.