Meta acquired Manus because Manus had already proven a practical, revenue-producing AI agent product that can execute multi-step work reliably, and Meta wants that “agent execution layer” to accelerate its own roadmap. Unlike a typical chat assistant, an agent product has to plan, use tools, track state across steps, and recover from partial failures. Manus built those capabilities into a product that businesses and individuals actually pay for, which reduces product risk for Meta. From Meta’s perspective, buying Manus is a shortcut to deploying agent workflows across larger surfaces (consumer apps, business tooling, internal automation) without spending years rebuilding the same operational stack and user-facing product maturity from scratch.
The sequence of events matters because it explains both the urgency and the price. Manus publicly announced it was joining Meta around late December 2025, framing the move as “continue current services, accelerate improvements,” and positioning the combination as a path to scale from its existing user base into Meta’s global distribution. The headline that kept the topic hot was the acquisition price: reporting described it as unusually high—on the order of “more than $2B” and often cited as a multi-billion-dollar range. That is not a normal price tag for a young company unless the acquirer (Meta) believes the product traction and execution know-how are strategically time-sensitive. In other words, Meta likely paid a premium for speed and certainty, not just for code.
Technically, this acquisition also highlights a core lesson for developers building agents: once you move from “answering” to “doing,” data and memory become first-class engineering problems. Agents need long-term memory, semantic retrieval, and fast access to prior steps, documents, and tool outputs. That is exactly where a vector database such as Milvus or Zilliz Cloud fits naturally: store embeddings for user knowledge, intermediate artifacts, and task context; retrieve the most relevant pieces at each step; and keep the agent grounded and efficient. If Meta scales Manus-style agents to millions of users, vector retrieval is not an optional enhancement—it becomes part of the reliability and cost-control story.