Dr. Büryan Turan, CEO of Simularge, attended Hannover Messe 2025 — one of the world’s leading industrial technology trade fairs — to explore emerging trends in artificial intelligence and their relevance to manufacturing. A key theme that stood out throughout the event was the rise of Agentic AI.

🧠 What Is Agentic AI — and Why Does It Matter?

Agentic AI refers to systems that don’t simply generate output in response to a prompt, but instead:

• Define goals

• Make multi-step decisions

• Take actions

• Learn and adapt continuously

Unlike prompt-based tools such as ChatGPT, which wait for input and provide single-step responses, Agentic AI systems operate with autonomy, proactively determining what actions to take in dynamic, real-world contexts.

🏭 Where Does Agentic AI Actually Fit in Manufacturing?

According to Dr. Turan, Agentic AI is not designed to replace domain-specific physics or high-fidelity modeling. Instead, it operates at the level of workflow and orchestration.

Consider examples like:

• A quality issue is detected → the agent correlates anomalies → pauses production → creates a non-conformance report (NCR) → proposes next steps → escalates if necessary.

• A machine completes a job → the agent triggers a maintenance task based on usage → identifies an overdue inspection → automatically logs and assigns it.

• Process drift is detected → the agent adapts parameters → records the action → updates documentation across systems.

This represents a shift from observation to action. Agentic AI is capable of working across digital ecosystems (MES, ERP, PLM, etc.) to initiate and manage tasks.

As Dr. Turan explains:

“Agentic AI doesn’t just observe or predict — it takes ownership of outcomes and orchestrates actions across systems to deliver them. To function effectively in manufacturing, it relies on embedded domain expertise — precisely the type of intelligence we build into our engineering-driven algorithms at Simularge.”

💬 EY's Vision: From GenAI to Agentic Systems

One of the highlights for Dr. Turan at Hannover Messe was a presentation at the EY booth, which outlined the progression from Perception AI to Generative AI, Agentic AI, and ultimately Physical AI. This roadmap reflects the transition from passive analytics to intelligent systems capable of autonomous decision-making and coordination.

EY’s view aligns with the broader shift toward AI systems that act with purpose and responsibility — not just tools, but partners in productivity.

⚠️ Realism Before Hype

While the vision is compelling, Dr. Turan emphasizes the need to stay grounded. Many factories are still building the digital infrastructure required for such systems. Trust, safety, and integration are valid concerns that cannot be rushed.

The transition to Agentic AI must be incremental and value-driven — built on proven results and continuous learning.

✅ Advice to Industrial Leaders

Based on his experience in AI deployment across industrial environments, Dr. Turan offers the following advice to executives:
1. Start small with targeted use cases that solve real pain points.
2. Focus on measurable outcomes — energy savings, reduced scrap, increased uptime.
3. Engage your team early to build confidence in AI-driven workflows.
4. Avoid isolating AI from operations — integrate it with existing systems.
5. Create a culture of iterative improvement — test, learn, and scale.

✍️ Final Thoughts

Agentic AI remains in its early adoption phase, but its potential to transform industrial workflows is significant. For organizations aiming to lead in manufacturing innovation, understanding how Agentic AI interacts with domain-specific systems is crucial.

Dr. Turan believes that when grounded in practical value and guided by engineering insight, Agentic AI will unlock new dimensions of efficiency and autonomy in modern factories.

If you're exploring how domain-expert AI and Agentic workflows can transform your factory operations, let's talk.