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.

Manus utilizes a technique called chain of thought injection, allowing agents to actively reflect and update their plans, enhancing stability and reasoning capabilities during complex tasks.

Manus can accomplish a wide range of real-world tasks, excelling in creating travel itineraries, detailed financial analyses, and educational content, while also assisting with database compilation and high-quality presentations.

The executor agent in Manus combines outputs from completed subtasks into a final synthesized output for the user, showcasing its sophisticated dynamic task decomposition algorithm.

However, Manus faces challenges with coordination among specialized agents as tasks scale in complexity, which can hinder its effectiveness.

Manus exemplifies these trade-offs well, as its multi-agent orchestration significantly reduces per-task costs to around $2, compared to competitors like OpenAI's Deep Research.

Project Mariner allows users to enter tasks that agents can execute in the background, enhancing productivity by managing multiple tasks simultaneously.

Manus provides greater transparency and user control, allowing users to inspect, customize, or replace individual sub-agents and tool integrations, which centralized platforms often lack.

Our multi-agent system starts with a supervisor agent that orchestrates tasks, understands user intentions, and delegates to specialized sub-agents, ensuring high accuracy and reliability.