Agentic AI Revolutionizes Robots, Making Them Smarter and Safer

Agentic AI is revolutionizing the world of robotics by turning traditional machines into smart, adaptable systems that can think, learn, and act on their own. Unlike older robots that followed fixed instructions, these new systems can handle unexpected changes, work safely with humans, and improve over time.

Puneet Tiwari, Chief Technology Officer at ARAPL Ltd, explains that traditional robots and Autonomous Mobile Robots (AMRs) relied on static maps, rule-based planners, and offline programming pipelines. While they excelled when the real-world environment matched their programmed model, even minor deviations, like a spill on the floor or a blocked aisle, often forced the system into a predefined 'fail' state, requiring human intervention.

In the industrial sector, there has been a significant increase in the adoption of robotics across various applications. Traditional robots are valued for their reliability and precision, but they struggled to adapt to dynamic work environments, posing safety concerns. The integration of Agentic AI allows robots to make autonomous decisions, plan, and adapt to dynamic workspace actions in real-time. This enables them to perform more versatile tasks and operate effectively beyond controlled settings with minimal human intervention.

Prateek Jain, co-founder and COO of Addverb, notes that with Agentic AI, robots can perceive their environment, make real-time decisions, and adapt to new situations. This allows them to handle complex tasks while human workers focus on strategic operations. For instance, Addverb’s AMRs and Robotic Sorters can now autonomously plan optimal routes, adapt to changing warehouse conditions, and effectively collaborate with other robots or human workers.

Similarly, in Amazon’s fulfillment centers, intelligent robots navigate complex warehouse layouts, avoid obstacles, prioritize tasks, and adapt routes without direct human intervention. These robots do not just follow set paths but choose the most efficient one in real-time, operating independently.

Anurag Gupta, co-founder of STEMROBO Technologies Pvt. Ltd, highlights that with Agentic AI, robots can sense changes and decide what to do next. If something blocks their way or if the task changes, they don’t stop or crash but instead think through and take action. They use sensors, smart software, and learning tools to adjust.

For example, a robot in a school lab can find a new path if something is in the way or help another robot finish a task. The intelligence lies not just in detecting change but in responding with purpose. Agentic AI transforms robots from passive machines into proactive problem solvers capable of operating in uncertain and evolving conditions.

With Agentic AI, robots can also learn from experience, much like humans. If a robot encounters a challenge, figures out a better way to complete a task, or identifies a more efficient path, it can retain that knowledge and apply it in future scenarios. This knowledge can be shared, with one robot transferring its learning to others, creating a collaborative learning ecosystem.

Modern fleets employ federated and hierarchical learning, where they learn from their own experiences and share them with the group. Each robot logs interactions, which are aggregated centrally, turned into improved instructions, and broadcast back to the fleet, often overnight or even edge-to-edge. This 'collective experience' paradigm has been demonstrated in large-scale reinforcement learning deployments, where new facility layouts are mastered in days instead of weeks, and every unit benefits from each peer’s learnings.

With Agentic AI, robots are no longer just tools; they are becoming intelligent collaborators that learn, adapt, and evolve alongside human beings.