AI Has Hands Now. You Should Decide What It Can Do.

We’ve crossed a line.

AI is no longer just generating text.

It’s starting to take actions.

Your agent can now:

• call APIs


• delete data


• send emails


• trigger workflows


• modify production systems

And in most setups today, it does all of that without a control layer.

The uncomfortable truth

Most AI agents today operate like this:

await deleteUser(userId);

await sendEmail(customer);

await transferFunds(amount);

If the agent decides to do it — it just… runs.

No checkpoint.

No approval.

No policy enforcement.

That’s fine in a demo.

It’s not fine in production.

This is where things break

The moment your AI touches real systems, you’re exposed to:

• Accidental destructive actions


• Prompt injection leading to unintended behavior


• Over-permissioned tools


• No audit trail of decisions

You don’t need a malicious AI.

You just need:

a slightly wrong decision, at the wrong time, in the wrong environment

The missing layer: decision before execution

What’s missing is simple:

Every action should be checked before it runs.

Not after.

Not in logs.

Not in alerts.

Before execution.

Introducing Runplane

Runplane adds a runtime control layer between your AI and real-world actions.

Instead of executing immediately, every action goes through a decision:

• ✅ ALLOW


• ❌ BLOCK


• ⏸ REQUIRE APPROVAL

What this looks like in practice

Without control:

await deleteUser(userId);

With Runplane:

await runplane.guard(

  "delete_user",

  "production-db",

  { userId },

  async () => {

    return deleteUser(userId);

  }

);

Now that action can:

• be blocked


• require human approval


• be logged and audited

This is not just theory

If you’re building:

• AI agents


• automation workflows


• MCP-based tools


• internal copilots


• API-triggering systems

You already have the problem.

You just haven’t felt it yet.

Built for modern AI architectures (including MCP)

Runplane fits directly into current AI stacks:

• Works alongside agent frameworks


• Supports MCP-style tool execution flows


• Sits between the model and the tool call


• Does not require changing how your tools work

It doesn’t replace your agent.

It controls what your agent is allowed to do.

Why this matters now

We’re moving from:

“AI suggests actions”

to:

“AI executes actions”

That shift changes everything.

Execution without control is risk.

Try it (free developer tier)

We opened a free developer tier so you can test this in real flows:

https://runplane.ai/auth/sign-up?mode=developer

You can:

• integrate in minutes


• simulate risky actions


• test approval flows


• see exactly how decisions affect execution

Final thought

You don’t need to wait for a disaster to add control.

By the time something breaks, it’s already too late.

AI has hands now.

You should decide what it’s allowed to touch.