Lack of Experts

The AI landscape is rapidly evolving, with significant advancements in large language models (LLMs) and generative AI. However, the integration of multiple AI agents, known as a mixture of agents or experts , is still in its nascent stages.

As it requires requires sophisticated coordination among different models which adds layers of complexity to the system. Running multiple AI agents simultaneously demands significant computational resources .

Efficient scaling and interoperability between agents sometimes lead to degrade in the quality of inference it produces and extensive integration issues .

Consumers also faces issues like highly personalized experiences, also single-agent systems or non-interactive agents are more vulnerable to errors and adversarial attacks ; which can also leads to bias and fairness issues . The possibility of consumer getting the best & desired inference get less as been of non-interactive and single agent inference system which leads to suboptimal consumer experience.

Koboto network solves and address all the problems by paving a way for agents to interact in simplistic Plug n Stitch manner where they can leverage each other expertise in order to provide the desired inference the consumer wants . Koboto network implements it creating its own foundational agents and koboto SDK which comprises of various tools which application agents can use such as koboto identity system, Rate limiting, defensive distillation and anomaly detection system.

Koboto makes Mixture of agents more robust, flexible, secure and fast in action by integrating multi-interactive, Inference aggregation and intent based agents which application agents and domain coordinator can integrate to find the best inference from koboto and the other ai agent-based networks.