By Dr. Andrei Belitski (NLP & Agentic AI Lead) and Jules Antoine Goffre (Chief Solutions Officer)
Agentic AI is on everyone’s lips. In this article, we break down the myths, highlight real opportunities, and explain who’s best positioned to win in this emer ging space. We’ll also share Symanto’s journey in becoming a key player in Agentic AI integration—and what we bring to the table.
Agentic solutions refer to software platforms, service systems, or smart devices (like robots or autonomous vehicles) that think and act independently—or semi-independently. When these systems interact with each other in a coordinated way, they’re referred to as multi-agent systems. The end goal is to mimic human teams: delegating tasks, working together, and requiring minimal human supervision.
Yes—they’re not just a concept. Visit Los Angeles to see food delivery robots navigating sidewalks or take a ride in a robotaxi in San Francisco. While those are physical robots, agentic solutions also exist in enterprise settings—and have for over 20 years.
A prime example: digital twins. These are simulations of real-world systems (like factories or supply chains), made up of autonomous agents (e.g., warehouses, trucks, customers). These agents follow rules, interact with each other, and can even integrate machine learning to simulate decision-making. Companies use digital twins to stress-test scenarios, optimize performance, and improve planning. Platforms like AnyLogic have pioneered embedding Machine Learning algorithms into these models.
The current excitement around agentic solutions is driven by advances in Natural Language Processing (NLP), especially large language models (LLMs) like GPT.
Language is central to nearly all AI applications—from customer care to software development and robot programming. Even autonomous driving relies on language labels to interpret the environment (e.g., “car,” “pedestrian,” “snow”).
LLMs with GenAI interfaces brought a major shift: they can now understand context, retrieve knowledge, and generate relevant outputs—making everything we touch potentially more intelligent. As much as GenAI has become a daily companion and a co-pilot for consumers, managers and employees, GenAI has struggled to be adopted successfully to address complex business problems. The shortcomings are multiple:
Agentic AI improves over GenAI by:
For example, an agentic pipe might include the following (not exhaustive):
Most successful implementations require a Center of Excellence within organizations—collaborating with some of the partners above. Relying on a single vendor to “do it all” rarely works.
What has been Symanto’ s journey to become an AI Agentic Domain Integrator?
Founded in 2010—well before today’s AI boom—Symanto has always focused on the intersection of psychology, NLP, and business analytics, with a mission to “understand people at scale.” Over the years we have stayed true to this mission statement, even if our delivery models had to adapt very quickly and undergo a profound transformation. Our delivery models evolved:
One of the key reasons for our success has been our very close ties to academia, to always be on the cutting edge and an ability to understand how to apply these technologies in an applied way on real use cases.
Over the last 15 years, we have built and updated a library of modular, reusable components that are highly re-usable in the domains of:
The quick and seamless access to these modules and components, together with the experience we gathered in managing AI agents has been a game-changer for the development and deployment of our own solutions. This makes us a true Agentic AI domain integrator. Recent launches include the SymantoAssist’s suites of solutions addressing Call Center AI related use cases as well as our Agentic AI Accelerator, helping companies to quickly build scalable use cases using best-in-class tools. These and more will be featured in our upcoming newsletters.
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