Manus performs complex, goal-driven work by coordinating planning, execution, and validation across multiple steps. Instead of returning a single answer, Manus attempts to finish the job you assign it. For example, if asked to research a topic, it can gather sources, summarize findings, and organize them into a structured report. If asked to build a simple application, it can generate code, adjust files, and iterate based on intermediate results. This behavior is what defines Manus as an AI agent rather than a conversational assistant.
The attention around what Manus does increased sharply after Meta acquired the company. Meta’s decision to pay an unusually high price highlighted the value of Manus’s execution-focused design. Rather than treating autonomy as a demo feature, Manus built systems to manage retries, partial failures, and long-running tasks in production. For Meta, which operates products used by billions of people, these operational characteristics matter more than novelty. The acquisition suggests that Meta sees agent execution as a practical capability that can be embedded across products, developer tools, and internal workflows.
Behind the scenes, Manus’s ability to “do” work depends heavily on structured memory and retrieval. Agents must recall relevant information at each step without overwhelming the model with unnecessary context. This is a natural use case for a vector database such as Milvus or Zilliz Cloud, where embeddings enable semantic search over documents, logs, or prior actions. By retrieving only the most relevant data, the agent can stay efficient and accurate. Manus’s design demonstrates how vector search becomes a core component of agent execution rather than an optional optimization.