SuperAGI

most independent agents are capable of doing very thin slice of use case, but for complex knowledge work tasks, more often than not, one agent is not enough. You need a team of agents. We introduced a concept of Agent Clusters - which operate in master slave architecture and coordinating among themselves to complete nuanced tasks and coordinating via shared memory and shared task list. Another big bottleneck I think is lack of a notional concept of Knowledge for Agents. We have LTM and STM, but knowledge is specialized understanding of particular class of objectives ( ecommerce customer support, Account based marketing, medical diagnostics for particular condition etc ) plugged into the agent. Currently agents leverage on the knowledge available in the LLMs. LLMs are great for intelligence, but not necessarily knowledge required for an objective. So we added concept of knowledge - which is a embedding plugged into agent apart from LTM / STM There lot of other challenges that need to solved like agent performance monitoring, agent specific models, agent to agent communication etc to truly solve for agents deployed in production. Not sure about point mentioned in the article that they might even take over the entire stack because autonomous agentic behaviour is good for certain use cases and not for all kinds of apps.