🔄 Iteration Process
In this section, we will explore three different approaches for the iteration process. In this context, iteration means self-improvement. We want our agents to self-improve and adapt over time, just like humans learn from experience.
1. Reinforcement Learning​
Through reinforcement learning, after each task, agents receive rewards based on success metrics, allowing them to refine their strategies over time. This self-learning process ensures agents become more efficient and adaptive with every iteration—just like a skilled human gaining experience, our agent can learn to handle tasks more efficiently.
What makes our product unique is its ability to autonomously generate AI agents, plan complex systems with our Meta-Agent, and continuously improve through reinforcement learning—all with minimal human intervention.
2. Mind Evolution​
Our Meta-Agent uses real-world data to evaluate agent performance based on actual task results. It doesn’t just rely on predefined rules—it actively analyzes real outputs to assess effectiveness.
First, it generates a diverse set of agents, each with unique strategies for solving a given problem. These agents are then scored based on their performance metrics, such as accuracy, efficiency, and task success rate. Using evolutionary selection, the system identifies top-performing candidates from the distribution of scores and pairs them to generate new, improved "children" agents through a genetic-style optimization process.
The system then integrates these new agents back into the group, rescoring all agents and repeating the process until the most optimal agent emerges. This continuous trial-and-error evolution ensures that the best-performing AI agent is always selected and ready to execute tasks with maximum efficiency.
3. Long-term Memory​
Long-term memory allows agents to retain knowledge and experiences from previous tasks, enabling them to apply this information to future tasks. This approach ensures that agents do not start from scratch each time they encounter a new task. Instead, they build upon their past experiences, leading to more informed decision-making and improved performance over time.
By leveraging long-term memory, our agents can recognize patterns, recall successful strategies, and avoid past mistakes. This continuous accumulation of knowledge helps agents become more proficient and effective in handling complex tasks, ultimately leading to better outcomes and higher efficiency.
By combining these three approaches—Reinforcement Learning, Mind Evolution, and Long-term Memory—our agents can continuously self-improve and adapt, ensuring they remain at the forefront of AI capabilities.