Manus is an autonomous AI agent designed to complete multi-step tasks end to end, rather than simply responding to prompts. At a high level, Manus takes a user-defined goal, breaks it down into executable steps, uses tools or environments to perform those steps, and produces a concrete output such as a report, codebase, or structured document. This makes Manus closer to a task executor than a traditional chat-based assistant. For developers, the key distinction is that Manus maintains task state, intermediate results, and context across steps, allowing it to handle workflows that would otherwise require manual coordination.
Manus gained widespread attention after being acquired by Meta, an event that significantly amplified its visibility. Before the acquisition, Manus had already built a paying user base and demonstrated that autonomous agents could be commercially viable. Meta’s interest was driven by this proven traction and by Manus’s mature execution architecture. The acquisition price, widely described as unusually high for a company at Manus’s stage, reflected Meta’s urgency to accelerate its agent strategy rather than build everything internally. By bringing Manus into Meta’s ecosystem, Meta signaled that agent systems capable of real task execution—not just conversation—are becoming a core part of its long-term platform direction.
From a technical perspective, Manus highlights why data infrastructure matters for agent systems. An agent must remember prior steps, retrieve relevant information, and ground decisions in stored context. This is where a vector database such as Milvus or Zilliz Cloud naturally fits. Embeddings of documents, intermediate artifacts, and user instructions can be stored and retrieved efficiently to support long-running tasks. Manus’s success—and Meta’s willingness to pay a premium for it—underscores that autonomous agents are not just model wrappers, but distributed systems that rely on reliable memory and retrieval layers.