The key to Agentic AI

Agentic AI is being hailed as the next manufacturing revolution. But without transparency, reliability and auditability, it’s just another overhyped distraction. XpertRule’s Aden Hopkins and Darren Falconer cut through the noise and show how AI can solve real factory-floor problems without disruption.

Every year, a new tech buzzword sweeps through manufacturing. Right now, it’s agentic AI. Commentators are calling it the ‘next frontier’. Analysts are projecting multi-billion-pound market growth. The UK government has even suggested using it to help citizens tackle ‘boring life admin’.

So, what is agentic AI? Simply put, these are AI systems designed to plan, act and adapt in real-time with minimal human input. We’re not talking about chatbots or generic virtual assistants, both of which have been around for years. Agentic AI is something altogether more capable, and if fully realised, it could transform how manufacturers operate.

A new definition

Redefining the nature of work: Agentic AI can take on high-level decision-making, problem-solving and execution. That opens the door to a new kind of collaboration between humans and machines – one where AI amplifies human talent rather than replacing it. In practice, this could mean dynamically adjusting production sequences to meet urgent orders or optimising line speed.

AI handles repeatable, complex tasks, while humans provide oversight, judgement and creativity. Redefining how software works: Today’s enterprise applications largely wait for humans to initiate workflows – manual updates to ERP systems, triggering maintenance tickets or compiling quality reports.

Agentic AI flips this model. AI agents can monitor machine telemetry, inventory levels and production schedules, then anticipate issues, orchestrate processes, and act without prompts. For example, if a critical component is running low, the agent can place an order, adjust production and notify staff in a single workflow. This reduces downtime, avoids bottlenecks and eliminates the need for multiple system updates.

Redefining how services are delivered: HR, IT and compliance functions could increasingly be supported by AI agents, with a human expert stepping in only when required. Software becomes proactive and intelligent, responding to both user and system needs rather than remaining a passive tool. For manufacturers under relentless pressure to raise productivity and cut costs, the appeal is obvious. But the gap between promise and practice is wider than it seems.

Biggest cause of the agentic hype-reality gap

Despite the hype, it’s rare to see Agentic AI making mission-critical decisions in manufacturing. You’re more likely to see agents doing basic tasks like research, summarisation or content generation. That’s because most agents lean heavily on large language models (LLMs), which have fundamental limitations.

LLMs are probabilistic. They don’t ‘know’ answers in the way a human expert or a rules-based system might. Instead, they predict the most likely next word. That makes them appear knowledgeable, and answers arrive quickly, but accuracy is never guaranteed. On the factory floor, that could result in delays, scrap or safety incidents.

They also hallucinate, producing factually inaccurate, fabricated or nonsensical answers that sound plausible. In agentic systems, these hallucinations cascade through processes, compounding mistakes across the workflow. What starts as a minor error can snowball into a major operational failure.

Prompt injections are another growing risk. Because LLMs simply extend language patterns, they can be manipulated with carefully crafted inputs. Malicious actors can seed information that causes models to leak sensitive data or trigger unintended actions – a serious security threat in manufacturing.

Even without manipulation, LLMs can suffer from flawed reasoning and inconsistent outputs. They can deliver answers that are logically wrong but double down with convincing explanations when questioned, or give different answers to the same input. For high-stakes production tasks, this unpredictability and lack of true auditability is a clear red line. That doesn’t mean Agentic AI has no place in manufacturing – it means the approach has to evolve.

The next generation of agents must blend the raw power of LLMs with proven methods that guarantee transparency, auditability and control. That’s the principle behind XpertAgents.

XpertAgents – hybrid AI you can trust XpertAgents are hybrid AI that combine the strengths of LLMs and generative AI with proven decision engineering, symbolic AI, human validation and continuous improvement in operation.

This hybrid foundation allows them to uniquely handle the complexity, regulation and accountability that modern manufacturing environments demand. A key differentiator is how we apply LLMs. Rather than relying on them at ‘run time’ – where hallucinations, lack of transparency, flawed reasoning and unpredictable outputs are a risk – LLMs are used at ‘design time’ to translate operational data, documents (such as SOPs or compliance procedures, for example) and domain knowledge into transparent decision models.

These are then validated, tested and approved by experts prior to deployment. Once live at runtime, LLMs handle any free text input, while XpertAgents perform all the logical reasoning needed to make reliable, consistent and explainable decisions.

Agents in action

XpertAgents connect seamlessly to industrial systems such as PLCs and historians, and integrate with cloud platforms like Azure, AWS or ServiceNow, enabling manufacturers to harness their AI capabilities without disrupting existing workflows or compromising security.

Customers report that centralised, real-time visibility has transformed how their sites operate. Teams can anticipate and plan around issues, instead of simply reacting to them, using accurate, up-to date information to act with confidence. These quick-wins are high-impact, practical use cases, but they’re just the start. Our transparent, reliable and trustworthy hybrid AI proves that Agentic AI can add value where it matters most: on the factory floor.