Skydive vs ChatGPT Workspace Agents

Workspace Agents are reusable workflow agents that run inside ChatGPT with strong enterprise governance. Skydive gives each agent its own cloud computer, identity, memory, model, and a presence across web, Slack, email, and text. OpenAI centralizes governed workflows; Skydive gives each role an independent worker.

ChatGPT offers mature governance: role-based access, audit surfaces, per-action write approvals, end-user versus agent-owned auth, and connector constraints. Skydive matches the human-in-the-loop need with training mode and adds isolated per-agent sandboxes, a credential proxy, and visibility tiers.

Both support triggers. OpenAI's trigger API returns a conversation URL and, in beta, a run id you can poll, but the agent's final output is not retrievable through the API. Skydive agents run on schedules, can serve their own webhook endpoints, and return messages, files, and live app links you can use directly.

Workspace Agents are in research preview, run on OpenAI models, and live inside ChatGPT. Skydive is self-serve, lets you choose the model per agent across many providers, and gives each agent its own computer, hosting, and channels. No migration is needed to run them side by side.

Not quite. OpenAI documents Workspace Agent memory as per end user, not shared across everyone using the agent. Skydive supports team-shared agent memory, so authorized teammates can build on the same learned context, alongside each agent's own persistent memory.

Each agent runs in its own isolated sandbox, and credentials are injected through a proxy so raw tokens stay out of prompts, logs, and memory. OpenAI offers mature enterprise governance inside ChatGPT; Skydive is currently in beta, and security and compliance details are available on request.