What is mcp runtime for ai agents?
An MCP runtime gives an MCP-capable client durable execution and state. The client (Claude Desktop, Cursor, ChatGPT, or OpenCode) reasons and chooses tools; the runtime provides bounded execution, durable memory, schedules, approvals, and an audit trail. It turns an interactive client into something that can run, remember, and be overseen.
What it isn't
Honest boundaries
Defining a category means saying what it is not — so adjacent tools aren't conflated.
- Not the model — the client brings its intelligence; the runtime brings durable execution.
- Not a replacement for a hosted agent team — it is a second way to reach the same durable runtime.
- Not stateless tool-calling — the runtime persists memory, tables, and results across turns.
From
An MCP client that is smart within a session but forgets everything when it closes — no schedules, no durable state, no oversight.
To
The same client connected to a durable runtime: workflows run within configured bounds, state persists across sessions, and every action is recorded and approvable.
What to look for
A buyer's checklist
The questions that separate a runtime built for production from a tool built for demos.
Bounded execution
Does it execute workflows with scoped tool access and clear controls — or just forward tool calls to your laptop?
Persistent state
Can the agent remember between turns via memory, tables, and artifacts that survive restarts?
Schedules + oversight
Can it run work on a schedule, pause for approvals, and record an audit trail?
A dashboard
Can you watch runs, read traces, and step in — or is the client a black box?
Where OrgSDK fits
OrgSDK is an MCP runtime. Connect your client over MCP and it gets the same durable capabilities as hosted agent teams: bounded execution, durable state, schedules, approvals, and a dashboard. The MCP page lists the clients that connect and how.
See the product