Can AI validate your technology decisions before you implement them? 

More and more organizations are moving beyond GenAI experimentation and asking where it can deliver tangible value. The question is no longer whether large language models work, but how to embed them into business processes while maintaining control over quality, costs, and accountability for outcomes. Read what actually determines the effectiveness of an agentic architecture. 

Agentic AI – systems made up of multiple collaborating agents that perform complex tasks –  is increasingly part of the conversation around practical GenAI applications. Demonstrations of such solutions can be impressive. What is much harder to determine, however, is how they will perform in a real-world business environment. 

Experts from Sii’s Digital Competency Center decided to put this to the test. In an internal experiment, they examined which factors actually determine the effectiveness of an agentic architecture and which decisions have the greatest impact on implementation success. 

A demo is not production 

Most agentic systems perform best in demonstrations. A model analyzes documents, identifies dependencies, and produces ready-to-use recommendations. The real challenge begins when the solution encounters new data, previously unseen document formats, or more complex business scenarios. 

In a production environment, three additional factors come into play: 

• the scale of the data being processed, 
• variability in input data, 
• accountability for the decisions made. 

This is the point at which promising pilots need to be tested against production requirements and business value criteria. 

How did we test the limits of Agentic AI? 

The experiment involved 23 Solution Architects from Sii’s Digital Competency Center. Five teams were given the same task: build a multi-agent system that could support the development of a solution concept based on extensive documentation for an enterprise-class system implementation. 

The teams were free to choose their: 

• architecture, 
• models, 
• frameworks, 
• tools. 

On the second day, the solutions were tested using new documents that the participants had not seen before. No additional prompt tuning was allowed. The results were assessed by a technical and business evaluation panel. 

The key takeaway? Every team built a working end-to-end solution, but the quality of the outputs varied significantly. Simply proving that the system worked was therefore not enough to define success. It demonstrated that the approach was technically feasible, but not necessarily economically viable. 

6 decisions that matter more than your choice of framework 

The experiment showed that solution quality is not determined by the framework alone. Decisions made much earlier in the design process have a far greater impact, including: 

• selecting a model based on the data, costs, and regulatory requirements, 
• establishing a shared language for concepts, categories, and business rules, 
• determining the number of agents and the appropriate level of architectural complexity, 
• defining a context management strategy, 
• introducing human-in-the-loop checkpoints, 
• monitoring the cost of each case, document, or decision. 

The most practical conclusion is straightforward: more agents, a larger context window, or a more popular model do not automatically produce better results. Each decision needs to be evaluated in terms of quality, cost, risk, and accountability for the outcome. 

“The first question should not be which model or framework to choose. First, you need to establish whether Agentic AI solves a specific business problem and whether you can measure the solution’s quality, cost, and risk,” – says Dariusz Adamowski, Senior Director CC Digital, Sii Poland.

Agentic AI does not have to make decisions for people 

One of the most compelling applications observed during the experiment was the use of Agentic AI as a decision validation mechanism. 

Instead of automatically generating solutions from scratch, the system can act as a second set of eyes. It can review an existing concept, compare it against requirements, identify potential risks, and generate questions worth asking before a decision is made. 

This approach could be applied to areas such as: 

• evaluating an architecture proposed by a vendor, 
• analyzing tender documentation, 
• technology due diligence, 
• reviewing technical documentation before modernizing an IT environment. 

How can organizations prepare for Agentic AI implementation? 

The findings from Sii’s experiment point to one practical direction: preparation for Agentic AI implementation should start with understanding the business process, not with selecting the technology. 

The key is to determine: 

• where Agentic AI can deliver tangible value, 
• how quality should be measured, 
• how costs should be monitored, 
• which decisions should remain in human hands. 

In practice, the organizations that gain the greatest advantage will be those that treat Agentic AI not as a one-off experiment, but as part of a well-designed, measurable, and responsibly managed business process. 

Download the full analysis: Agentic AI in practice

The full material covers: 

• how the experiment was conducted, 
• the evaluation methodology, 
• all 6 decisions, along with recommendations, 
• practical Agentic AI applications, 
• an approach to preparing for and validating implementation.