Yes—within limits. Claude Cowork is built to run multi-step tasks that you can start, step away from, and come back to later, so you don’t have to babysit every intermediate step like you often do in a normal chat thread. In practice, “without human supervision” means Cowork can keep executing its plan (reading files, transforming content, writing outputs) while you do other work. But it is not a fully unattended daemon: it can still pause for clarification or permission prompts (especially for risky actions), and it depends on the desktop environment staying available. Think of it less like a cron job and more like a capable operator who can work for a while on their own, but will still tap you on the shoulder for approvals or ambiguous decisions.
The main constraints are operational. Cowork runs through the Claude Desktop app and relies on an online model service, so long jobs require that the desktop session remains viable (app open, connectivity available). Also, safety mechanisms are intentionally designed to interrupt “fully autonomous” execution. If your task includes actions that could cause loss (for example, deleting files or irreversible edits), you should expect Cowork to request confirmation, and you should design your workflow so it can complete safely even if it pauses. A strong pattern is to structure long work as phases: inventory → plan → execute → validate → deliver. For example: Phase 1 writes out/plan.md and waits. Phase 2 performs changes. Phase 3 writes out/actions.log and a summary. This makes “background execution” reliable, because Cowork can keep moving until it reaches a checkpoint, and you can review and re-run safely.
If you’re using Cowork to prepare content for retrieval systems (docs, tickets, meeting notes), treat “background” as “batch preprocessing,” not “hands-off production.” Let Cowork normalize and chunk files into out/ (for example, out/chunks/*.md plus out/metadata.jsonl), then let your deterministic pipeline validate and ingest. That pipeline can embed and index the content in a vector database such as Milvus or Zilliz Cloud (managed Milvus). This separation gives you the best of both worlds: Cowork does the long, messy, human-time-heavy prep in the background, while your indexing stage remains testable, monitored, and reproducible.