Let’s start with an example: a day in the service center of a global OEM. Early in the morning, a field technician receives a notification on his tablet: “Hydraulic valve C on pitch-system in asset E03_16 shows abnormal pressure values. Expected failure in three days. Replacement part already on site. Maintenance recommended now.” The technician taps on the recommendation and instantly receives a visual step‑by‑step guide, enriched with insights from archived tickets and previous service cases. No searching, no guesswork, no hotline. Within minimum time, the technician completes the job — his tenth first‑time closure this week, a record. This seamless process was not orchestrated by a well‑coordinated back‑office team, but by an AI system, Agentic AI, capable of acting and making decisions autonomously.
Many experts now consider Agentic AI the next evolutionary step in artificial intelligence — a field that only recently entered our daily lives with the rise of large language models and chatbots. Unlike AI applications such as Robotic Process Automation (RPA) or simple chatbots, which merely execute predefined instructions, agentic AI systems act like experienced problem solvers: they analyze a situation, independently plan a series of actions, execute them, and continuously learn from the outcomes.
Put simply, it is like moving from a rigid decision tree to a virtual team of specialists that anticipates, reasons, and adapts. Too good to be true?
What sounds like a giant leap in theory faces several challenges in practice. Organizations must establish adequate data foundations and IT structures, integrate AI applications into existing processes, implement guardrails such as human‑in‑the‑loop mechanisms, and choose suitable technologies and solutions.
Although vendors and tech outlets tout a paradigm shift, Gartner advises caution over inflated expectations, noting that many initiatives begin too early, lack clear goals, or depend on legacy technologies. At the same time, real‑world use cases are emerging where Agentic AI already delivers tangible value — for example, in production and logistics.
A closer look reveals: Agentic AI is not a magic bullet, but it has the potential to become a key technology — provided the prerequisites and expectations are aligned.
On an enterprise level, the technology offers significant potential. Agent systems take on specific tasks along the entire value chain — in production, maintenance, or technical service. They work in a context‑aware manner, interact with machines, systems, or humans, and help automate processes, reduce response times, and measurably improve operational efficiency. According to Gartner, by 2029 around 80% of all customer service requests will be handled without human intervention — by AI agents that independently analyze, decide, and act.
Some companies are already embracing this shift. Microsoft, for example, uses agent‑based systems in cloud incident response to detect IT issues early, trigger countermeasures automatically, and involve human experts only for complex exceptions. The result: faster response times and lower operating costs.
The potential of agentic technologies can be beneficial for many industries, from the shopfloor to the product. Companies like Amazon or Bosch integrate Agentic AI into their production, logistics, and service processes to address operational challenges such as increasing complexity, skilled labor shortages, and unpredictable downtimes.
Were Agentic AI is already creating value:
In modern factories, agents continuously analyze machine data, detect deviations early, and automatically trigger predictive maintenance actions. This minimizes downtime, optimizes maintenance intervals, and strengthens human‑machine collaboration.
Example: Manufacturing companies use Agentic AI to automate preventive maintenance. Sensors detect subtle defects, and the agent schedules the maintenance team and secures spare parts — without a single phone call.
In modern logistics networks, speed has become a decisive competitive advantage. Agentic AI plays a key role by evaluating data from inventory levels, routes, and customer orders in real time and responding instantly to changes. This enables supply chains to be adjusted dynamically, bottlenecks to be avoided, and transport routes to be planned more efficiently.
Amazon demonstrates how AI agents autonomously manage inventory, adjust supply chains, and optimize transport routes in real time. Robots like Proteus and Vulcan make their own decisions to make operations more efficient.
A major industrial OEM and operator relies on Agentic AI to transform its field service operations. The goal: resolve complex issues faster through guided diagnostics — ideally in a single visit. The system provides access to structured instructions, historical service data, and automatically documents the technician’s work.
For Agentic AI to deliver on its promise, it needs more than powerful models. The systems depend on high‑quality, connected, and context‑rich data. This includes structured data from ERP, CMMS, or MES systems, as well as unstructured content from manuals, service logs, or sensor streams. Organizations aiming to deploy Agentic AI within a data‑driven factory model typically need to begin by investing in their data strategy, for example through centralized platforms like Databricks or by developing domain‑specific knowledge bases.
Beyond technical prerequisites, Agentic AI demands an organizational shift. Decisions previously made by humans are delegated to autonomous systems unfamiliar to the workforce. Roles change, processes evolve. Without active change management, resistance is likely – especially if employees question the decision‑making authority of systems.
Trust in the technology must be earned. Transparent communication, early involvement, and continuous feedback are key success factors.Concepts like human‑in‑the‑loop not only strengthen processes but also build transparency and trust: humans retain oversight while agents handle routine tasks or make recommendations. This interplay is crucial, particularly in safety‑critical environments.
Insights from industry and IT show: Agentic AI, especially in the form of multi‑agent systems, has tremendous potential to make operations more robust, efficient, and scalable.
Unlike purely rule‑based AI, agent‑based systems are tailored to specific business processes, interact with existing data sources, and pursue clearly measurable goals.
It is important to highlight that these are complex, often business‑critical systems. Organizations starting out should not rely on off‑the‑shelf solutions but develop targeted approaches with expert support.
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