Agentic AI in Action: Transforming Manufacturing with Autonomous Systems

By Stuart Kerr, Published 28 June 2025, 07:06 BST

The manufacturing sector is undergoing a profound transformation, driven by agentic AI—autonomous systems capable of making decisions without human intervention. These intelligent systems are optimising production lines, reducing costs, and enhancing efficiency in industries from automotive to electronics. As companies face global competition and supply chain pressures, agentic AI is emerging as a critical tool for staying ahead. Drawing on insights from industry leaders and recent advancements, this article explores how these systems are reshaping manufacturing, their real-world applications, and the challenges of widespread adoption.

The Rise of Agentic AI in Manufacturing

Agentic AI refers to systems that can independently analyse data, make decisions, and execute tasks in dynamic environments. Unlike traditional AI, which follows predefined rules, agentic AI adapts to changing conditions, making it ideal for complex manufacturing processes. A 2024 report by McKinsey & Company estimates that AI-driven automation could boost global manufacturing productivity by up to 30% by 2030. At the forefront of this shift is LandingAI, a California-based firm specialising in visual inspection systems. Their platform, LandingLens, uses agentic AI to detect defects in real time, achieving a 98% accuracy rate in automotive part inspections, according to a 2024 case study published by Forbes.

Andrew Ng, founder of LandingAI and former head of Google Brain, highlights the technology’s transformative potential. “Agentic AI allows manufacturers to move beyond static automation to systems that learn and adapt on the fly,” Ng told MIT Technology Review in 2025. This adaptability is crucial in industries where precision and speed are paramount, such as semiconductor production, where even minor defects can cost millions.

Real-World Applications and Industry Impact

In practice, agentic AI is streamlining manufacturing workflows across the globe. At Foxconn, a major electronics manufacturer, AI-driven robots equipped with Snowflake’s Cortex AI platform optimise assembly lines for smartphones. A 2025 Bloomberg report detailed how Foxconn reduced production errors by 25% by integrating Cortex AI, which uses natural language processing to interpret real-time data from sensors and adjust machinery settings autonomously. This partnership, announced in March 2025, exemplifies how agentic AI can enhance scalability in high-volume production.

In the UK, Rolls-Royce has adopted agentic AI for aerospace manufacturing. Their AI system, developed in collaboration with the University of Cambridge, predicts maintenance needs for jet engine components, reducing downtime by 15%, according to a 2024 study in Nature Communications. “These systems don’t just follow instructions; they anticipate problems and propose solutions,” said Philip Wood, Rolls-Royce’s chief technology officer, in a statement to The Engineer. Such advancements align with broader AI-driven efficiencies, as explored in our related article on AI-powered healthcare innovations.

Challenges in Adoption

Despite its promise, agentic AI faces significant hurdles. High implementation costs remain a barrier, particularly for small and medium-sized enterprises (SMEs). A 2024 Deloitte survey found that 60% of SMEs cite budget constraints as a primary obstacle to adopting advanced AI systems. Additionally, the complexity of integrating AI with legacy infrastructure poses technical challenges. “Retrofitting older factories with agentic AI requires significant investment in both hardware and training,” notes Sarah Blackman, a manufacturing technology analyst at Gartner, in a 2025 IndustryWeek interview.

Workforce implications are another concern. While agentic AI boosts efficiency, it raises fears of job displacement. A 2024 International Labour Organization report estimates that AI automation could affect 20% of manufacturing jobs globally by 2030. However, experts like Blackman argue that AI creates new roles, such as AI system supervisors and data engineers. Rolls-Royce, for instance, has retrained over 500 employees to work alongside AI systems, focusing on upskilling rather than redundancies.

Data security is also critical. Agentic AI systems rely on vast amounts of real-time data, making them vulnerable to cyberattacks. A 2025 Cybersecurity Ventures report highlighted that manufacturing is the second most-targeted industry for data breaches, with 30% of incidents linked to AI system vulnerabilities. To address this, companies like Snowflake are implementing end-to-end encryption and federated learning protocols to protect sensitive data.

Expert Perspectives and Ethical Considerations

The ethical implications of agentic AI are under scrutiny. “Autonomous systems must be transparent to maintain trust,” says Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory, in a 2025 Wired interview. Rus advocates for explainable AI, where systems provide clear reasoning for their decisions, particularly in safety-critical industries like aerospace. This aligns with efforts to address the “black box” problem, where AI outputs lack interpretability, as discussed in our article on AI’s legal and ethical challenges.

Diversity in AI development is another priority. Systems trained on biased datasets can perpetuate errors, such as misidentifying defects in products from underrepresented markets. A 2024 study in Nature Machine Intelligence found that diverse training data improved AI accuracy by 12% in global manufacturing applications. Companies like LandingAI are partnering with international suppliers to ensure datasets reflect varied production environments, addressing this gap.

The Future of Agentic AI

Looking ahead, agentic AI is poised to evolve further. Advances in multimodal AI, which combines vision, language, and sensor data, are enabling more sophisticated applications. A 2025 IEEE Spectrum article highlighted how multimodal systems at Intel’s chip factories predict equipment failures with 95% accuracy, reducing unplanned downtime. Additionally, collaborations like LandingAI and Snowflake’s are expanding AI’s reach, with plans to integrate agentic systems into supply chain management by 2026.

Regulatory frameworks will shape adoption. The European Union’s AI Act, set to take effect in 2026, classifies agentic AI in manufacturing as high-risk, requiring rigorous safety and transparency standards. “Compliance will be a challenge, but it’s necessary to ensure trust,” says Blackman. Industry leaders are also advocating for global standards to harmonise AI deployment across borders.

Conclusion

Agentic AI is revolutionising manufacturing by enabling autonomous decision-making, improving efficiency, and reducing costs. From Foxconn’s smartphone assembly lines to Rolls-Royce’s jet engines, these systems are proving their value. However, challenges like cost, workforce transitions, and data security must be addressed to unlock their full potential. As Andrew Ng notes, “The future of manufacturing lies in AI that works alongside humans, not in place of them.” With continued innovation and ethical oversight, agentic AI will redefine the industry, driving a new era of intelligent production.

Sources: Forbes (2024), MIT Technology Review (2025), Bloomberg (2025), Nature Communications (2024), The Engineer (2024), Deloitte (2024), IndustryWeek (2025), Cybersecurity Ventures (2025), Wired (2025), Nature Machine Intelligence (2024), IEEE Spectrum (2025).