Agentic AI Solutions: Hype or Reality?

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. 

Form 

What Are Agentic Solutions? 

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. 

Are Agentic Solutions Already Real? 

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. 

Why the Hype Now? 

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: 

  • They struggle with reliability at scale: lack of access to fit-for-purpose data and are bound by the size of contextual information. 
  • They hallucinate facts. 
  • They lack human-level reasoning. 
  • They can’t always handle multi-step tasks. 

Form 

How Agentic Solutions Address These Gaps 

Agentic AI improves over GenAI by: 

  1. Breaking down complex problems into smaller tasks, and 
  2. Assigning the right tool or agent for each task—not always an LLM. 
 

For example, an agentic pipe might include the following (not exhaustive): 

  • The Web-scraping agent collects real-time data online. 
  • The data engineering agent cleans, disambiguates and extracts the relevant text fragments and stores all data into processable storage (e.g. Vector DBs, RAG pipelines). 
  • An LLM is prompted to detect relevant topics in documents. 
  • A simple calculator is called in to perform arithmetic calculations to generate KPIs from the fragments. 
  • A reasoning model (like O3 or DeepSeek-R1) handles logical chain-of-thought tasks (e.g. root cause analyses) 
  • The Supervisor agent checks outputs for quality and prevents shortcuts (e.g., only finding the first match on a website). 

Form 

Who Are the Key Players?  

We at Symanto believe that multiple types of players will have a role to play in the agentic landscape: 
 
  1. Foundational AI Providers 
    These include companies building LLMs, GenAI platforms, and reasoning models. They’re creating the infrastructure, but usually target broader sets of use, rather than solving individual use cases specifically 
  2. Agentic-Enabled SaaS Platforms 
    These are tailored to specific problems (e.g., drug discovery, supply chain automation). They’re powerful and plug and play, but often hard to integrate, customize or extend.
  3. Consulting & Professional Services 
    These firms bridge the gap between business needs and technical implementation. They support with transformational and change agenda aspects as well as upskilling. 
  4. Agentic Domain Integrators 
    These companies, like Symanto, bring domain expertise and reusable NLP components to accelerate use case development. They work directly with clients to design and implement scalable, domain-specific agentic pipelines.  
  5. IT Services Providers 
    These companies’ strengths lie in the broader integration into the IT landscape. They have distinctive knowledge in application layer integration as well as IT architecture and cover all peripheral needs around cybersecurity and ensuring robustness of the IT infrastructure. 
  6. AI Infrastructure Ecosystem 
    Cloud providers, data ops teams, resource managers, and platform providers all play a vital role in supporting the ecosystem. 
 

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.

Common Pitfalls in Agentic AI Implementation 

  1. Over-automation 
    Not everything needs to be autonomous. Human-in-the-loop is essential—especially early on. Like autonomous cars, the journey starts with assisted systems before going fully autonomous. 
  2. Treating It as an IT Project 
    Agentic AI should be driven by business outcomes, not just technology. Ownership must come from business leaders, with clear objectives. 
  3. Chasing Productivity Only 
    Focus not just on cutting costs but also on revenue growth, differentiation, and product innovation using agentic capabilities. 
  4. Lack of Organizational Skills 
    Companies need broad AI literacy—from leadership down. Decision-making can’t be outsourced to a tech team three layers down. 
  5. Stuck at Proof-of-Concept 
    Build with scalability in mind from the start. Integrate properly with internal systems to go beyond pilots and insular solutions. 
  6. Wrong Build vs. Buy Strategy 
    Use existing platforms when possible, but be ready to build custom components for your competitive edge. Core teams should focus on managing the ecosystem, not building everything in-house as tempting as it might be. 
 
 

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: 

  • from delivering insight projects, 
  • to then deliver SW products in the form of dashboards, reports and self-service platforms and APIs, 
  • to afterwards deliver scalable data platforms with quick access to the latest and best tech stacks, addressing complex business problems,  
  • to finally adopt an agentic approach covering not only insights but also interactive features.  

 

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: 

  • Consumer and customer-facing domains 
  • Competitive intelligence and value creation
     

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.