> ## Documentation Index
> Fetch the complete documentation index at: https://docs.manthan.systems/llms.txt
> Use this file to discover all available pages before exploring further.

# 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.