Claude Cowork currently has several important limitations because it is a research preview feature that runs only in Claude Desktop on macOS, not on the web or mobile, and it comes with operational and compliance constraints that differ from normal chat. Practically, that means you can’t assume it will be available on every device you use, and you shouldn’t design a workflow that depends on cross-device continuity. Cowork sessions are also designed for “task execution,” which can be longer-running and more stateful than chat, so the experience depends on keeping the desktop app open and having a working internet connection. If your workflow assumes “start on laptop, finish on phone,” or “handoff to another machine,” Cowork in its current form is not built for that.
There are also limitations that matter specifically for teams and regulated environments. Cowork stores its conversation history locally on your computer, and its activity is not captured in certain enterprise audit/export mechanisms. In other words, it is not positioned as a compliance-first workflow surface. The official guidance is to avoid using Cowork for regulated workloads. For developers, treat Cowork as a powerful local productivity tool, not a governance boundary. On top of that, Cowork’s agentic behavior means results depend heavily on instruction quality. If you say “clean up this folder,” you’ll get inconsistent outcomes because “clean” is subjective; if you say “create out/manifest.csv listing every file, then move only .png files into images/ by month, and do not overwrite originals,” you’ll get far more predictable behavior. This is less a limitation of “capability” and more a limitation of natural-language ambiguity: you must specify scope, invariants, and outputs.
Finally, Cowork’s limitations show up when you try to make it part of a production pipeline. Cowork can prepare and normalize data, but it is not a substitute for deterministic ingestion, validation, and monitoring. A good architecture is to keep Cowork upstream as a “content shaping” layer and keep your real system downstream. For example, have Cowork standardize documentation into chunk-ready Markdown plus JSON metadata, then run your normal embedding + indexing pipeline into a vector database such as Milvus or Zilliz Cloud (managed Milvus). This keeps your retrieval stack reproducible and testable while letting Cowork reduce manual prep time. If you treat Cowork as the source of truth instead of a preprocessing helper, its current preview-stage constraints (platform scope, compliance gaps, and instruction sensitivity) become hard blockers.