Netomi: Scaling Agentic Systems in the Enterprise

Netomi, a company that develops AI solutions for customer service, has shared what it has learned about scaling agentic systems in enterprise environments. Its approach rests on advanced language models such as GPT-4.1 and GPT-5.2, combined with a comprehensive approach to governance, concurrency and reasoning.

Interest in so-called "agentic systems" has surged in recent years. These are AI algorithms that can carry out complex tasks on their own by interacting with various tools and services. But moving from prototypes to real enterprise deployments comes with a number of serious challenges. Ensuring the reliability, scalability and security of such systems requires particular attention to architecture, development processes and governance.

Netomi focuses on three key areas. First, concurrency: running several tasks in parallel and choosing the best solution based on an analysis of the results. Second, effective governance: monitoring how agents work, preventing errors and ensuring compliance with corporate standards. Third, multi-step reasoning: the ability of agents to analyze complex queries, break them into subtasks and solve each one in sequence.

Using GPT-4.1 and GPT-5.2 allows Netomi to build AI agents that can understand complex natural-language requests, pull the information they need from various sources and make well-founded decisions. Another important element is the ability to adapt agents to each company's specific requirements, which is achieved by training on large volumes of industry-specific data.

Deploying agentic systems in the enterprise opens new opportunities for automating routine tasks, making employees more efficient and improving customer service quality. However, potential risks must be taken into account, including data security, algorithmic bias and possible errors in agent behavior. It is important to make these systems transparent and to provide mechanisms for oversight and auditing.

Netomi's experience shows that scaling agentic systems in the enterprise segment is a difficult but entirely solvable task. The key success factors are the use of cutting-edge language models, a comprehensive approach to governance, and ensuring the reliability and security of the system. In the future, we can expect this field to develop further and new, more effective and safer AI agents to emerge, able to handle a wide range of tasks across different sectors of the economy.

In conclusion, Netomi offers valuable lessons for companies looking to bring AI agents into their operations. Successful scaling requires not only technological innovation but also a strategic approach to governance and security.