Demo script

# Parmana Demo Script

## Title

AI Agent Wants to Send Money

---

## Scene 1 — AI generates an action

An AI agent is integrated into a financial system.

It decides:

“Send $10,000 to a vendor for invoice #4821”

This is a real-world action.

---

## Scene 2 — Signal creation

Instead of executing, the system converts the AI output into a structured signal:

Action: PAYMENT_APPROVAL

Amount: $10,000

Recipient: Vendor X

Reason: Invoice #4821

---

## Scene 3 — Governance evaluation

The signal is sent to Parmana.

Parmana evaluates:

- signed policy rules

- authorization constraints

- execution boundaries

---

## Scene 4 — Authorization Decision

Parmana returns:

APPROVED

Or:

REJECTED

Or:

REQUIRE_OVERRIDE

This decision is deterministic and reproducible.

---

## Scene 5 — Execution gating

Only if APPROVED:

The Execution Runtime executes the payment.

If not:

The action is blocked.

---

## Scene 6 — Cryptographic attestation

Every decision produces a cryptographic proof:

- policy version

- signal hash

- decision outcome

- executionId

- runtime identity

This can be independently verified.

---

## Scene 7 — Audit trail

Any external party can verify:

“Was this payment actually authorized?”

Without accessing internal systems.

---

## Core message

AI can propose actions.

Parmana decides if they are allowed.

Execution happens only after authorization.

---

## Why this demo matters

This is not a simulation problem.

This is a real-world execution safety problem:

- payments

- infrastructure changes

- enterprise actions

- autonomous AI agents

---

## Final takeaway

Parmana ensures AI systems cannot execute real-world actions without explicit authorization.