The multi-agent architecture includes supervisor agents, specialized sub-agents, and human-in-the-loop workflows to automate investment research.

An agent is something a human can delegate tasks to, and we're aiming for agents to handle more complex tasks over time.

The supervisor approach decomposes complex customer queries across specialized agents, allowing us to achieve higher accuracy levels in our responses.

Companies are focusing on workflows and multi-agent systems to mimic human decision-making processes in AI.

Manus operates as a multi-agent AI system, coordinating a team of sub-agents to complete various tasks, from travel planning to financial analysis, rather than relying on a single neural network.

The technology behind this AI agent is a multi-agent system that utilizes a core react-like loop and various tools to assist users in coding tasks.

Independently evaluating sub-agents helps identify weak links and improve overall accuracy in the system.

An AI agent is defined as a system that allows a language model to autonomously decide how many times to run processes until it finds a resolution, rather than following a predefined path.

The development of AI agents involves creating systems that can autonomously handle tasks and communicate issues back to users for resolution.