You can use manus for tasks that require planning, persistence, and coordination across multiple steps. Common use cases include research and analysis, generating structured documents, automating repetitive workflows, and building small software projects. Manus is particularly useful when a task would otherwise require you to manually switch between tools, track intermediate results, and ensure nothing is missed. By acting as an agent, Manus takes responsibility for execution rather than leaving orchestration to the user.
Interest in these use cases grew after Meta acquired Manus, because the deal suggested that such agent-driven workflows are moving toward mainstream adoption. Meta’s unusually high acquisition price reflected confidence that autonomous agents will become a standard interface for complex work. By integrating Manus into its broader ecosystem, Meta can apply these capabilities to internal operations, developer tools, and potentially consumer-facing products. This context makes Manus more than a niche productivity tool; it becomes an example of where AI systems are heading.
Many of these use cases depend on effective knowledge retrieval. For example, an agent generating reports or code often needs access to prior documents, specifications, or historical context. A vector database such as Milvus or Zilliz Cloud enables semantic search over this information, allowing the agent to pull relevant context at each step. This makes workflows more accurate and scalable. Manus’s design illustrates how agent use cases and vector databases align naturally, especially as systems grow in complexity and scope.