ITTech Pulse Exclusive Interview with Linir Zamir, Team Lead-AI Engineer at Telit Cinterion

Welcome to ITTech Pulse! We chat with Linir Zamir is a PhD in Computer Science leading AI innovation at Telit Cinterion, specializing in autonomous systems and edge intelligence.

Can you walk us through your career journey and how it led you to your current role?

I’ve always been drawn to computers and the power of intelligent systems. This led me to pursue a Master’s and PhD in computer science at Florida Atlantic University, where I focused on AI and autonomous systems. Early in my career, I worked on integrating AI with consensus algorithms and blockchain technology to develop scalable methods like ISRaft and Localized-State-Change algorithms that ensure secure and efficient communication among autonomous systems.

Today, I lead all AI development initiatives at Telit Cinterion, including the deviceWISE Intelligence Suite and Visual Inspection systems. My work focuses on integrating advanced AI technologies to enable intelligent automation, edge analytics, and autonomous industrial agents that bring active intelligence to factory operations. Over the past few years, this trajectory has allowed me to combine my experience in AI, edge computing, and industrial automation to drive real-world impact in factories by helping operators make faster and smarter decisions while optimizing processes at scale.

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How does the Intelligence Suite platform allow AI agents to learn the logic and behavior of PLCs, CNCs, robots, and sensors without manual configuration?

The deviceWISE Intelligence Suite leverages autonomous industrial agents that can explore and understand devices across the factory floor. These agents observe operational patterns, analyze interactions between machines, and map workflows dynamically without requiring manual programming or configuration. Essentially, the agents “learn by watching”: they infer logic, behavior, and interdependencies across PLCs, robots, CNCs, and sensors. This builds contextual understanding of the environment, a capability that allows them to detect anomalies, recommend corrective actions, and even autonomously adjust operations while maintaining synchronization with human operators.

What technical problems did you need to solve so the AI agents could “see, think, and act” in factories?

The primary challenges revolved around three areas: data integration, real-time decision-making, and human trust. Factories generate massive volumes of heterogeneous OT and IT data, so we needed robust orchestration to unify this information and ensure agents could access it seamlessly. Next, enabling real-time reasoning and autonomous action required designing high-performance agents capable of making safe decisions at the edge, even with partial information. Finally, human acceptance was critical: operators must understand and trust the AI. We addressed this by providing transparent workflows, real-time decision support, and tools that clearly show why agents take specific actions – basically, turning a “black box” system into a collaborative co-pilot.

How do you predict AI-driven industrial systems will evolve over the next five years, especially as factories become more connected and AI-native?

I see factories becoming increasingly autonomous, with AI agents acting as real-time coordinators across every machine and workflow. Edge intelligence will expand to allow for faster and localized decisions while maintaining security and minimizing latency. We’ll also see greater synergy between human operators and AI; the AI handles repetitive, data-intensive tasks, which frees humans to focus on strategic decisions and innovation. Finally, AI will evolve to be more predictive and prescriptive: anticipating equipment failures, dynamically optimizing processes, and autonomously adapting production lines to shifting demands.

What common challenges arise when manufacturers integrate smart automation, and how do you address them?

Integration challenges often include legacy system interoperability, operator trust, and data privacy. Many manufacturers want to keep sensitive operational data on-premises, so the deviceWISE platform is designed to run edge-based AI agents that provide advanced intelligence without requiring cloud deployment. We also invest in training and interfaces that make AI actions interpretable to ensure operators feel confident collaborating with autonomous systems. Finally, we provide modular, standards-based protocols like MCP to ensure our agents can communicate seamlessly across diverse industrial environments.

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What ensures your AI agents remain reliable and safe while making important decisions in real time?

Reliability comes from combining thorough data analysis with safety constraints embedded in every agent. The agents continuously monitor multiple data sources (like sensors, PLCs, robots) and cross-reference anomalies against operational rules. Decisions are always made within predefined safety bounds, and human operators have oversight for confirmation where necessary.

Edge deployment further reduces latency and risk, which enables real-time responses without relying solely on cloud connectivity. Transparency and explainability are also key: operators can see the reasoning behind each action, which builds trust and ensures safe outcomes.

 As we wrap up, what final message would you share with manufacturers about the future of autonomous factories with deviceWISE Intelligence Suite?

The future of manufacturing is autonomous, intelligent, and highly adaptive. deviceWISE Intelligence Suite empowers factories to transition from reactive operations to proactive and self-optimizing systems. By combining real-time sensing, agentic AI, and seamless integration across all devices, manufacturers can dramatically reduce downtime, improve quality, and scale processes more efficiently.

My message to operators and engineers is that AI is not here to replace humans. It’s here to enhance capabilities, unlock new insights, and help factories achieve their full potential while keeping humans in the loop where it matters most.

Thank you, Mr. Linir, for sharing your insights with us.

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