The DeepSeek Business Creator demonstrates how to leverage DeepSeek's latent reasoning space to emulate three-agent interaction for multi-business creation on the web. This system integrates a simple Markov chain optimization process with browser automation, allowing businesses to emerge dynamically in an optimized sequence. The approach enables AI-driven enterprises to launch and scale efficiently, simulating real-world economic expansion with minimal human intervention. From an initial dataset of 200 business ideas, 20 were selected based on predefined success factors. Each of these businesses was assigned three AI agents, each specializing in different aspects of business creation: market research, operational strategy, and adaptive growth. The sequence of business creation was optimized using a Markov chain model, ensuring that dependencies between business types and market readiness were accounted for. This optimization increased the likelihood of success by structuring the order in which AI-driven businesses launched and scaled. AI agents interacted within DeepSeek’s latent space to generate business plans dynamically. These plans emerged from the interplay of three AI agents, refining concepts based on strategic reasoning. Once validated, browser automation was used to execute the launch of these businesses, coordinating their deployment across different online markets. As AI-driven businesses launched, they began to emerge in various markets worldwide. The system's ability to simulate economic scaling in a decentralized manner demonstrated the potential of AI agents to drive real-world business success autonomously. As businesses evolved, AI agents adapted and absorbed the most successful strategies. The agent absorption rule ensured that underperforming agents were phased out, while the most effective decision-making patterns were integrated into the next generation of business iterations.