Tempo de leitura: 2 minutos
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.
From chatbots to autonomous multi?agent systems: the fast-forward evolution of enterprise AI
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?
Industrial Agentic AI: Use cases and examples
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:
1. Production/Predictive Maintenance: moving beyond reactive maintenance
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.
2. Logistics: Autonomous real?time optimization
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.
3. Technical Service: Guided troubleshooting saves time and resources
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.
Discover why data strategy and AI agents go hand in hand - and what agentic systems require - in the full blog post by our IoT expert Device Insight:
