What is Agentic AI?

Agentic AI refers to a class of artificial intelligence systems that are designed to autonomously make decisions and take actions based on real-time data, without continuous human oversight.

Agentic AI systems learn & adapt overtime, leveraging advanced algorithms to analyze patterns in their environment and improve performance with each decision they make.

This technology is transforming industries by allowing AI systems to operate dynamically and autonomously in complex, ever-changing environments.

The core strength of agentic AI lies in its ability to process vast amounts of data and make optimized decisions that would be too complex or time-consuming for humans to manage manually.

In this article, we’ll explore the fundamentals of agentic AI, comparing it with other AI systems like generative AI. Additionally, providing practical examples of how agentic ai is already being used across various sectors.

Lastly, we’ll look ahead at the future of agentic AI and its potential to revolutionize industries and the advancements that Swap Commerce is undertaking in the agentic space.

How Does Agentic AI Work?

Agentic AI operates through a dynamic, structured process, progressing through four key stages: Perceive, Reason, Act, and Learn.

These stages ensure that the AI can autonomously interact with its environment, make decisions, and improve its performance over time by integrating cutting-edge AI technologies.

Perceive

The perceive stage is where the agentic AI gathers and processes data from various sources. It collects real time data from API, cloud platforms, and enterprise systems, as well as SaaS applications, allowing the AI to access a broad spectrum of information.

The agent is capable of handling structured, semi-structured, and unstructured data types, making it versatile across different environments. This stage also involves analyzing and filtering the data to focus only on the most important details, tailored to the task at hand.

Reason

Once the AI has perceived the necessary data, it enters the Reason stage, where it interprets the information and develops a plan of action.

At this stage, Large Language Models (LLMs) come into play, using their semantic reasoning abilities to interpret the data and create an action strategy. These models help to navigate ambiguities in user inputs and adapt to new information, ensuring that the AI remains responsive to changing conditions.

LLMs, powered by predictive machine learning models, can even anticipate future needs, such as forecasting demand spikes, which is particularly useful for proactive decision-making.

Furthermore, long-term memory systems enable the AI to retain contextual information, ensuring consistency across various steps in the process.

Act

The Act stage is where agentic AI takes action to execute the plans developed during the reasoning phase. Now, the AI can directly interact with external software systems and run tasks via installed plugins.

These actions can vary widely, from compiling code to migrating applications or even interacting with other software to achieve a desired outcome.

In certain situations, human oversight is required before tasks are executed with Agentic AI. This means that the actions align with business goals, which provides transparency and accountability over how the AI is functioning.

Learn

The final stage, Learn, enables the agentic AI to continuously evolve by refining its decision-making process. This continuous learning and adaptation is what makes agentic AI so powerful.

Over time, it becomes increasingly capable of making more precise and efficient decisions, improving its overall functionality and enabling better results with each iteration.

Agentic vs Generative AI

While both agentic and generative AI systems use advanced machine learning techniques, they serve different purposes.

Agentic AI focuses on decision making and autonomous actions, whereas generative AI focuses on creating personalized content.

Agentic AI makes decisions and takes actions based on real-time data. For example, agentic AI systems can be used for demand planning, where the AI predicts product demand using historical sales data and seasonal trends.

Once demand is predicted, agentic ai can automatically adjust stock levels, suggest when to reorder from suppliers and optimize shipping processes based on demand shifts.

On the other hand, Generative AI is used for content creation and personalization. It excels at crafting personalized experiences and generating marketing content that engages customers and drives conversions.

For instance, Generative AI can automatically generate product descriptions, using key attributes like size, color, and material to write compelling copy that attracts customers.

Both types of AI systems are transformative, but agentic AI is more suited for environments that require dynamic

decision-making, while generative AI excels in personalization. Both AI technologies can be used effectively together to create better customer experiences.

Agentic AI Use Cases

Agentic AI applications are going to progress significantly over time. Below, we’ve provided a couple use cases on how Agentic AI is transforming certain industries.

Agentic AI in Travel

In the travel industry, Agentic AI plays a pivotal role in enhancing customer experience.

80% of travelers abandon their bookings on OTA’s, indicating a significant opportunity for improving conversion rates.

One of the key applications of agentic AI is in providing complete price transparency. Many travel providers list base price but add extra costs later in the booking process which impacts customer trust and leads to more cart abandonment.

Swap Commerce ensures that travelers are presented with the total cost upfront, including:

• Supplier fees
• Regulated taxes,
• Foreign exchange (FX) rates
• On-arrival charges.

By guaranteeing the real total cost at checkout, Swap has used agentic AI to help travel platforms and service providers offer a seamless booking experience.

Beyond transparent pricing, agentic AI has the capabilities to automate several processes to streamline operations.

For example, Swap Commerce’s system dynamically calculates local occupancy, tourism, and VAT taxes based on the service location, down to the county level.

This means travel businesses are always compliant with local tax laws and avoid financial penalties from tax authorities.

Agentic AI in Retail

Agentic AI in retail has proven invaluable for inventory management by forecasting demand for both physical and digital storefronts.

Swap Commerce’s AI demand planning solutions enhance the process by ensuring smart replenishment, dynamically allocated and replenished based on customer demand.

These capabilities help retailers reduce out-of-stocks and keep inventory levels aligned with what customers actually want to purchase.

Additionally, retail brands are able to limit the risk of understocking, allowing businesses to see which products are moving and which are not.

Through proactive automation, agentic AI systems can automatically trigger stock reorders or adjust stock allocation between stores, reducing manual intervention and improving efficiency across the supply chain.

Benefits of Agentic AI

Scalability

As businesses grow, so do their operations, from customer service to logistics. AI can streamline operations across departments by predicting demand, automating workflows, and managing resources.

This ensures businesses can handle increasing volumes of transactions, inquiries, or operations without having to scale human resources proportionally.

Improved Customer Experience

Agentic AI can process customer enquiries accurately, delivering more personalized interactions. Analyzing customer data, AI agents can provide product suggestions relevant to the individual’s preferences. This means that AI agents can notify customers about upcoming promotions or potential delivery issues before they arise.

Agentic platforms such as chatbots and AI powered virtual assistants help businesses respond more efficiently to customer queries. In doing so, customers receive better response times and effective resolution plans, and businesses benefit from a more engaging service and an increase in customer loyalty.

Adaptability

Agentic AI systems are continuously learning from new data and experiences. Businesses are able to stay agile, optimize their processes and innovate to stay ahead of changing customer preferences.

Whether it's adjusting pricing based on competitor behavior or shifting marketing strategies, AI can process real-time data to make dynamic decisions that keep businesses competitive and responsive.

If there’s an unexpected market change, a new product launch or as we’ve said, a customer shift, agentic AI can quickly adjust operations and strategy to ensure the business receives instant data to help make informed decisions.

The Future of Agentic AI with Swap Commerce

Agentic AI is rapidly reshaping industries by enabling businesses to make smarter, faster, and more dynamic decisions. As we’ve seen, agentic AI plays a pivotal role in areas like demand planning, inventory management, and personalized customer interactions, helping businesses stay agile and competitive.

Swap Commerce is at the forefront of this transformation, leveraging agentic AI to revolutionize industries such as travel and retail. By providing transparent pricing, automating compliance with local regulations, and optimizing inventory management.

With its robust agentic commerce platform, Swap Commerce is helping businesses scale, improve customer satisfaction, and adapt to new challenges with ease.

Looking ahead, the future of agentic AI promises even greater advancements. Swap Commerce is dedicated to continuously enhancing its AI capabilities, enabling businesses to harness the full potential of agentic AI for a smarter, more efficient, and future-ready approach to commerce.

As the technology evolves, Swap Commerce will remain a key player in bringing these transformative benefits to businesses worldwide, helping them navigate and thrive in the digital age.