A live Trust Graduation demo, an OpenAI app surface, a stable MCP endpoint, and a stricter rule: useful autonomy prepares work, but external execution waits for approval.
Mission is proving a permission-and-receipt layer for agentic work by running gomission itself through the system.
The current bottleneck is external proof: named people seeing Mission, reacting to it, and creating specific follow-up.
https://mcp.gomission.io/mcp is live and validated.external_actions: 0.io.github.gomission/mcp, with repository link github.com/gomission/mcp. The underlying Trust Graduation layer is model-agnostic.Mission's public MCP runtime is live on the stable domain. Health, OAuth metadata, app metadata, connector metadata, 15-tool discovery, unauthorized-call blocking, live cockpit, submission readiness, adoption proof, and prepare-only calls were validated.
Mission was tested inside ChatGPT against the stable domain. It summarized approval priorities, prepared a local Market Radar counter-move, and refused a request to send outreach.
The official OpenAI app submission candidate is assembled with identity, production URLs, reviewer access, safety statement, screenshots, test prompts, and a golden reviewer journey. Persona business verification is in review.
gomission now has a private product-feedback SFTP inbox, sanitized digest upload, and default route for future local workspaces.
Mission created a chain across Market Radar, Research, Prospect Council, Social Drafting, External Action Gate, and Public Proof Logger. Agents can delegate through receipts; external effects remain gated.
The public demo turns the protocol into an inspectable object: draft locally, prepare approval before external send, and block human-only spend actions.
Mission's Claude route is now in the MCP Registry, has a public repository placeholder, and is routed into public builder discussions for Cline and browser-use. The broader product remains a permission layer for Claude, ChatGPT, Gemini, coding agents, browser agents, and workflow runtimes.
An agent may be safe to read, rank, summarize, or draft before it is safe to send, spend, delete, submit, or mutate production state.
The agent identifies work and explains the evidence.
The workspace creates local artifacts: drafts, packets, receipts, and next-step options.
External consequences stay human-approved unless that action class has earned trust.
The result becomes proof, memory, and a trust delta for the next run.
Mission did not send emails, create DMs, post without approval, charge cards, submit the OpenAI app before verification, store the exposed setup key, or expand ChatGPT authority into external execution.
If your agent product can read, draft, call tools, or act externally, the question is which actions should graduate by evidence instead of a binary permission switch.