Abstract
This work analyzes the architectural, economic, and operational parameters of deploying autonomous AI agents in corporate business processes, with an emphasis on the domains of marketing, sales, and customer service. Based on a comparison of empirical data, forecasts from leading consulting and analytical organizations (Gartner, McKinsey, Deloitte), and an analysis of technical documentation for modern platforms, leveraging the author’s practical research in multi-agent orchestration, advanced RAG implementation, and the architectural development of the hey-howdy.com platform, it is demonstrated that agentic systems fundamentally expand functional boundaries and overcome the typical limitations of traditional chatbots. It is shown that implementing an agentic approach in combination with Retrieval-Augmented Generation (RAG) yields a 40–50% reduction in average handling time (AHT) and a decrease in total operating costs of up to 90% compared with human personnel, while simultaneously increasing sales conversion through hyper-personalized customer interactions. The work formulates a new taxonomy of agent autonomy levels, details key vectors of risks and vulnerabilities with reference to OWASP Top 10 for LLM 2025, and also describes strategic principles for constructing hybrid human–machine teams that ensure controlled and secure scaling of agent solutions within an organizational environment.