Deciphering agentic AI for manufacturing
Key considerations and use cases
Manufacturing goes smart with AI
Manufacturing is experiencing a seismic shift from rigid, rule-based automation to truly autonomous, adaptive operations. While traditional digital transformation initiatives delivered greater visibility and deterministic control, they often fell short in enabling rapid, real-time decision-making during disruptions such as late deliveries or equipment breakdowns. Human intervention remains essential to interpret insights and drive action in the age of smart manufacturing.
The rise of agentic AI
Agentic artificial intelligence (AI) is fundamentally changing the game. As the next evolution of AI, agentic AI systems can act autonomously to achieve specific goals, moving beyond simply following preprogrammed instructions. These systems are transforming smart manufacturing from a data-rich to a decision-rich environment. Through goal-oriented reasoning, these systems function as “digital full-time equivalents” (FTEs), actively sensing, reasoning, negotiating, deciding and acting across interconnected manufacturing processes. Critically, these systems operate within stringent safety and quality parameters, with minimal need for human intervention.
Imagine a smart factory that doesn’t just react to problems, but anticipates failures, reroutes schedules, coordinates repairs and optimizes outputs, all done autonomously. This shift creates a self-healing and self-optimizing shop floor where intelligent agents collaborate to drive agility, efficiency and resilience.
AI agents: The foundation of agentic AI
As agentic AI transforms manufacturing, it’s important to distinguish it from traditional AI agents. AI agents are systems that sense and act to achieve specific tasks. They are task oriented and reactive, operating based on programmed logic or a machine learning model with limited autonomy. For example, a quality inspection AI agent uses computer vision to scan products on an assembly line and flags defects based on trained image recognition models. It performs a specific task (defect detection) and reacts to visual input but does not adapt its strategy or collaborate with other systems autonomously. Agentic AI, on the other hand, refers to advanced AI systems that exhibit higher degrees of autonomy, proactivity and adaptability. These systems can set subgoals, plan multistep actions, collaborate with other agents or humans, and learn from feedback to improve over time, rather than simply executing predefined tasks. Agentic AI systems operate as single agents or as networks of multiple agents. The difference between single-agent and multi-agent systems is defined below:
For operations spanning multiple ISA-95 standard levels and requiring real-time decisions to balance conflicting key performance indicators (KPIs), multiagent systems are becoming the clear choice.
High-potential agentic AI use cases for manufacturers
AI agents can be strategically deployed to automate workflows, collect and disseminate information, and monitor operations across an entire plant. By orchestrating these agents, manufacturers can unlock new levels of efficiency, agility and quality.
Examples of high-impact use cases include:
While the possibilities with agentic AI are vast, manufacturing leaders must prioritize the right opportunities by evaluating them across two key dimensions:
Relevance for agentic AI, which may consider these three factors:
- Coordination complexity: Does the process require orchestration across multiple systems, roles or decision layers?
- Real-time responsiveness: Is there a need for rapid, adaptive decision-making?
- Autonomy potential: Can the task be fully delegated to AI agents within defined guardrails?
Business value
- Impact assessment: What is the potential impact of the use case on efficiency, cost savings, quality or competitive advantage?
Key considerations for agentic AI adoption
Building effective agentic AI solutions requires a structured approach that integrates use case selection, architecture design, technical execution, and governance throughout development and deployment.
- Identify and prioritize use cases: Leverage a "relevance vs. business value" framework to identify high-impact use cases for pilot projects.
- Define modular, scalable agentic architecture: Specify agent roles, design interactions and ensure data readiness. Leverage containerized or microservices architectures and standardized frameworks for seamless agent onboarding and retraining.
- Implement shared context: Develop knowledge graphs for shared understanding and real-time event-driven coordination among agents.
- Establish supporting infrastructure: Equip factories with high-bandwidth connectivity, edge computing abilities, and low-latency networks to support real-time alerts and decision-making.
- Ensure governance, ethics and trust: Develop dashboards for human oversight, embed compliance checks and integrate supervisory controls within agentic AI workflows.
- Upskill workforce and drive change management: Deliver targeted training and proactive change management initiatives to empower employees to collaborate effectively with advanced AI systems and maintain operational continuity.
Accelerate your agentic AI journey with Deloitte
As operational complexity increases, AI-driven solutions are essential for sustained growth and efficiency. We stand at the forefront of this transformation, offering proven tools and industry expertise to accelerate your agentic AI adoption.
Looking for a detailed plan to guide your strategic implementation with a focus on governance, cultural change, and long-term value?
Check out our recent article on enterprise transformation for a step-by-step strategic roadmap.
Special thanks to Kreeti Mahajan, Parag Gajjar, Sarvotham Shetty and Debashish Chatterjee for their coauthorship of this article.
