Agentic AI refers to artificial intelligence systems that are designed to autonomously make decisions and take action. This allows them to pursue complex goals with limited human supervision. Unlike traditional AI models that require human input, Agentic AI combines the flexibility of large language models (LLMs) with the precision of traditional programming. This hybrid approach enables AI agents to analyze data, set goals, and execute actions independently, thereby enhancing productivity across various domains.

Typically, AI agents are designed for a specific function, and they perform best when their specialized capabilities are coordinated with other AI agents to create a collaborative, automated ecosystem.

While Generative AI excels at creating new content such as text, images, audio, video, and code, it relies on human input to define the context and goals of its output. Due to its focus on creation, generative AI is also prone to hallucinations, where it provides made-up answers or references to content that does not exist.

In contrast, Agentic AI is built to carry out complex, multi-step tasks on its own. These AI agents can assess their surroundings, make decisions, and proactively work toward predefined goals, such as maximizing sales, improving customer satisfaction, or optimizing supply chain processes. Generative AI is focused on the creation of new content, while Agentic AI is designed to execute specific tasks.

In healthcare, Agentic AI is revolutionizing personalized treatment and diagnostics. Companies have developed AI agents that can assist with daily tasks and provide personalized medication reminders to patients. Others can better prepare patients for pre-operative procedures by reminding them to fast or stop taking certain medication before their operation.

AI agents can be directly integrated into patients’ mobile devices, enabling fast, simple communication across various levels of technical literacy. These personal healthcare agents can become even more helpful when synchronized with wearable devices, using real-time information to offer additional insights and recommendations to patients. By orchestrating prescription information, electronic health records (EHR), and wearable data, Agentic AI can provide holistic, personalized assistance to patients at scale.

Retail companies are using Agentic AI to enhance their customer experience through digital assistants. AI assistants can streamline repetitive, basic internal processes for customer service associates, freeing them up to spend their time on harder scenarios and edge cases.

AI agents can further improve customer experience by offering personalized fashion recommendations or beauty advice tailored to customers’ preferences. Their ability to act also means that AI agents can handle a wider variety of tasks than generative AI chatbots. In this scenario, agent interoperability is key, as AI customer service agents might need to liaise with those in finance to enable purchases or those in reverse logistics to handle returns.

Agentic AI is being adopted in manufacturing facilities and warehouses to proactively suggest maintenance needs and detect workflow bottlenecks. When integrated with internal IT and enterprise resource planning (ERP) systems, these AI agents can monitor material inventory levels, generate purchase orders when supplies are low, and initiate short-term workflow adjustments to keep things running smoothly.

Digital twins of the warehouse environment can provide AI agents with real-time data—like product movement, robotic systems, and worker capacity—to further enhance their performance. Although human review of any purchases or maintenance requests is necessary to ensure quality, the AI agents’ ability to automatically create these suggestions produces significant efficiency gains.

Across supply chains, Agentic AI can streamline operations by autonomously predicting demand, managing inventory, and adjusting procurement strategies in response to volatile market conditions. These systems analyze historical data and real-time inputs to make smarter logistics decisions.

For example, logistics companies can seamlessly manage delivery routes and inventory levels. Due to their proactive nature, AI agents can also help manage supply chain disruptions. When an unexpected event occurs, such as geopolitical turmoil or climate disruptions, the AI can respond without having to wait for human instructions. It can recommend alternative routes or suppliers to keep operations moving. AI agents’ proactivity enables supply chain operations to be more agile and respond quickly to disruptions and changes that inevitably occur.

GS1 Standards can help enable the effective implementation of Agentic AI in supply chains. Standards provide a common language for identifying, capturing, and sharing information about products, locations, and entities. Like all AI, Agentic AI requires good-quality data to operate correctly, both for its initial training and for ongoing integration with digital systems. GS1 Standards support Agentic AI by ensuring data is accurate and can work across systems, helping AI agents communicate, coordinate, and function optimally.

As Agentic AI systems develop and are integrated into business workflows, a complex ecosystem of agents will emerge. AI agents from different companies, built for different niche purposes, will need to work seamlessly together to perform efficiently within their business environments. For example, within a supply chain, there could be an agent focused solely on procurement and another that manages warehouse inventory levels, while a third monitors meteorological data to proactively report disruptive weather events. All three of these agents will need to share data seamlessly to achieve real-time, proactive decision-making.

GS1 Standards offer a path to interoperability through a common language of identification for products, locations, and entities.