Manus orchestrates Claude workflows

Manus is not differentiated by owning the base model, it is differentiated by how it packages Claude into a long running worker that can keep track of a job without losing the thread. In practice, that means breaking a task into steps, deciding what context to show the model at each step, saving intermediate state outside the raw chat history, and feeding back only the pieces needed to finish the next action.1 2 3 4

• This is the same pattern emerging across serious agent products. Anthropic describes long running agents as needing compaction, progress files, and memory tools because each session can otherwise lose earlier work. The harness, not just the model, determines whether a multi step task actually completes.2 3 4
• A concrete example is Manus turning a broad request into a structured workflow, then spending credits on planning, browser actions, code execution, and API calls. That pricing model reflects the real architecture. The product is orchestrating many expensive substeps around Claude, not just sending one prompt and returning one answer.1 5
• This also explains why context engineering matters competitively, but is hard to defend forever. Perplexity, Raycaster, and other agent builders are converging on the same playbook, model agnostic orchestration plus proprietary workflow context. Once labs ship better native agent memory and tool use, the durable edge shifts toward domain workflows and user data.6 7 8

The next phase is a race to move from general purpose context management into workflow specific memory. The winners will be the products that remember how work gets done inside a category, not just the last few thousand tokens of a conversation.6 7 8