Manus AI works by running a continuous agent loop that alternates between reasoning and action. First, it interprets the user’s goal and creates a plan composed of smaller tasks. Then it executes each task using tools, environments, or code execution, while tracking intermediate results. After each step, Manus evaluates progress and decides what to do next until the goal is completed or a stopping condition is reached. This loop-based design allows Manus to handle tasks that require persistence, iteration, and conditional logic.
The Meta acquisition helps explain why this architecture matters. Meta did not acquire Manus simply for its models, but for its working agent system that had already been tested by paying users. Reports emphasized that the acquisition price was unusually high, which reflects the difficulty of building reliable agent orchestration at scale. Meta’s interest suggests that agent loops, state management, and execution control are now strategic assets, not experimental features. Integrating Manus allows Meta to accelerate its roadmap for autonomous systems across its platforms.
A critical technical component of how Manus works is retrieval-based memory. Each step in an agent loop may require recalling prior actions, documents, or external data. Storing this information as embeddings in a vector database such as Milvus or Zilliz Cloud enables fast semantic search when context is needed. This approach keeps prompts concise while preserving access to relevant knowledge. For developers building similar systems, Manus provides a concrete example of how agent logic and vector databases work together to create scalable, reliable AI workflows.