Agent Embeddings are vector-based representations of an agent's role, skills, and context. Instead of hardcoding which agent handles which task, the system uses these embeddings to find the best-suited agent for any incoming goal by comparing the task's requirements against what each agent is designed to do.
As the pool of available agents grows, manual task routing becomes impractical. Agent Embeddings make routing more intelligent and adaptive — the system finds the right agent based on meaning and capability, not just preset rules. This becomes especially valuable in large enterprise deployments where dozens of specialized agents may be in play.
