The function of the Manus is to act as an autonomous AI agent that completes tasks end to end by planning steps, using tools, and producing concrete outputs. In practical terms, Manus is designed to take a goal (“summarize these sources into a brief,” “draft a spec,” “generate a small web page,” “prepare a structured report”) and turn it into a sequence of actions with intermediate checkpoints. For developers, it helps to think of Manus as a workflow engine around an LLM: it wraps model reasoning with orchestration, state tracking, and tool execution so work can continue across many steps without you manually steering every turn.
That function is also why Meta cared enough to acquire Manus at a premium price. Meta’s products operate at massive scale, and “agent function” is only valuable if it is stable under real user load and can be integrated into larger ecosystems. Manus demonstrated the operational side of agents: handling queued tasks, managing concurrency limits, dealing with flaky external dependencies, and retrying or adjusting when a step fails. Those are the boring-but-critical pieces that separate an agent demo from something customers pay for. Meta’s acquisition—widely described as unusually expensive—signals that Meta is optimizing for speed to market and proven execution, not just raw research capability.
When you implement the “agent function” in real systems, the biggest hidden requirement is memory: the agent must retrieve the right context at the right time. If you simply dump everything into the prompt, you hit cost and context limits quickly, and performance degrades. A common pattern is retrieval-augmented execution: store documents and prior task artifacts as embeddings, then fetch only the top relevant items per step. A vector database such as Milvus or Zilliz Cloud is built for this pattern, providing scalable similarity search with predictable latency. If Meta is folding Manus into broader agent workflows, a robust vector layer is part of what keeps those workflows accurate, debuggable, and affordable.