By Stuart Kerr, Technology Correspondent
Published: 28 June 2025
Last Updated: 28 July 2025
Contact: liveaiwire@gmail.com | Twitter: @LiveAIWire
Author Bio: About Stuart Kerr
In the heart of tomorrow’s smart factories, a new kind of intelligence is quietly revolutionising production lines. No longer passive or reactive, this technology can make decisions, learn from its environment, and operate with minimal human oversight. It's called Agentic AI — and it's rapidly transforming how we build the world.
While traditional AI focused on prediction and automation within predefined boundaries, agentic AI goes further. These autonomous systems are designed to act independently in dynamic, real-world scenarios, continuously adapting to changing inputs and objectives. For manufacturing, this represents a seismic shift — where machines don’t just follow instructions, they interpret goals, collaborate, and optimise in real time.
From Assembly Line to Autonomous Decision-Maker
In traditional industrial systems, automation is tightly scripted. A robot may weld, sort, or assemble parts, but it requires constant calibration and context awareness from human supervisors. Agentic AI changes that. It enables systems to observe, reason, and act on their own — much like a human operator, but faster and often more reliably.
A recent report by Capgemini describes this transition as one of trust-based autonomy, where AI agents not only perform tasks but also determine how best to approach them within a set of constraints and ethical boundaries (PDF). These systems can react to disruptions in the supply chain, re-route production flows, and even halt operations if quality thresholds are breached — all without direct human input.
The Intelligent Factory Floor
Factories embracing agentic AI are already seeing measurable gains. At a leading European electronics manufacturer, agentic systems handle over 60% of process-level decision-making — from energy allocation to predictive maintenance scheduling. This isn't just automation, it's orchestration.
In one documented case, when a thermal sensor malfunctioned on a high-speed line, the AI agent isolated the fault, reconfigured the task sequence, and alerted a technician with a root-cause analysis. According to Automation World, such systems offer a new blueprint: collaborative intelligence where humans and machines solve problems together.
This harmonises with trends observed in broader logistics and infrastructure. As highlighted in Supply Chain Intelligence: How AI Is Rewiring Global Logistics, agentic systems can dynamically manage inventory, forecast bottlenecks, and adapt to shifting global demands with precision.
Building Smarter Machines with Smarter Minds
Under the hood, agentic AI relies on a constellation of technologies: reinforcement learning, large language models, vision systems, and intent-based programming. One technical paper from arXiv outlines how these components are integrated into industrial controllers capable of planning tasks and explaining decisions (PDF). This architecture turns dumb sensors and actuators into intelligent collaborators.
Companies like Tredence and Memeburn are already reporting success in applying agentic frameworks to reduce unplanned downtime, boost throughput, and eliminate waste in complex environments (Tredence, Memeburn).
A particularly innovative use case involves mobile robotic agents in construction zones. These bots coordinate with each other using shared task maps and environmental cues — not unlike those featured in Brick by Bot: How AI and Robotics Are Rebuilding Construction. This kind of decentralised intelligence dramatically reduces delays and on-site rework.
Smarter, Faster, Safer — But at What Cost?
As the technology matures, so do the questions. What happens when an agent makes the wrong decision? How do we embed ethical constraints into a system designed to operate independently? And who’s liable when things go wrong?
These concerns echo those raised in Smarter, Faster, Stronger: AI in Sports Training and Recovery, where agentic systems risk over-optimising for narrow goals. Ensuring explainability and aligning machine objectives with human values will be critical as adoption accelerates.
Governments and standards bodies are beginning to respond. Regulatory sandboxes are emerging to test the limits of autonomy safely. Meanwhile, partnerships between academia and industry — such as those discussed in arXiv’s framework — are developing benchmarks for accountability and system transparency.
The Road Ahead
Agentic AI isn’t a far-off idea — it’s already reshaping the factory floor. And just like past revolutions in steam, electricity, and computing, this one will rewrite the rules of production.
But the challenge now isn’t just building smarter machines — it’s ensuring they work in harmony with the humans who still design, oversee, and depend on them.
About the Author
Stuart Kerr is the Technology Correspondent for LiveAIWire. He writes about artificial intelligence, ethics, and how technology is reshaping everyday life. Read more