FinTechCryptoTask Prompt

Task Prompt to Verify Signed AI Agent Mandates (AP2, x401, Card Network) at Checkout

This Arc task prompt integrates verification of signed agent mandates (AP2-style intent and cart mandates, x401-style evidence objects, and card network agent credentials) into your transaction decision flow, on the rule that no protocol is trusted as a verdict. Arc, the deepidv credential gateway, builds intake and validation that parses each presented artifact, verifies signatures and chains, checks status and expiry where endpoints exist, and normalizes protocol differences into one internal mandate representation, then principal weighting that scores the enrollment behind each mandate (verifying party, evidence grade, recency, liveness) so a valid signature from a weak enrollment is treated as the risk it is. A scope-fit engine compares each transaction against the mandate's bounds on amount, velocity, category, merchant, and time and names the specific bound any miss violates, and composite decisioning fuses mandate validity, principal weight, scope fit, and transaction-time signals into approve, step-up, or decline, routing step-ups to a human-presence ceremony bound to the principal. An evidence writer records the artifacts, checks, scores, and outcome of each decision in an exportable, method-named record a network dispute or examiner can replay. Anything unverifiable routes to step-up rather than silent decline, so legitimate agents learn the door instead of the wall. Built for payments, fraud, and checkout engineering leads now receiving agent-initiated transactions.

How to use this prompt

1
Open Arc in the deepidv dashboard and paste the full prompt, or run it in Claude, ChatGPT, or Gemini to draft the integration design before wiring it to live checkout.
2
Add an INPUT section below the prompt listing the mandate formats agents present to you today, the status and expiry endpoints available for each, your existing transaction-time risk signals, and your current approve, step-up, and decline rules.
3
Run the prompt and review principal weighting first: a mandate is only as strong as the enrollment behind it, so confirm how each verifying party and evidence grade is scored before tuning scope fit.
4
Hand the normalized mandate representation and scope-fit engine to checkout engineering, route the step-up ceremony design to your identity team, and give disputes and compliance the evidence record format.
5
Re-run whenever you accept a new mandate protocol or card network agent credential, and replay a sample of recorded decisions each quarter to confirm the evidence holds up.

The prompt

Arc, integrate verification of signed agent mandates (AP2-style intent and cart mandates, x401-style evidence objects, and card network agent credentials) into our transaction decision flow.

Build:
1. Intake and validation: parse each presented artifact, verify signatures and chains, check status and expiry where endpoints exist, and normalize protocol differences into one internal mandate representation.
2. Principal weighting: score the enrollment behind each mandate, verifying party, evidence grade, recency, liveness, so a valid signature from a weak enrollment is treated as the risk it is.
3. Scope-fit engine: compare the requested transaction against the mandate's bounds (amount, velocity, category, merchant, time) and produce a fit verdict with the specific bound any miss violates.
4. Composite decisioning: fuse mandate validity, principal weight, scope fit, and transaction-time risk signals (device, velocity, history) into approve, step-up, or decline, with the step-up routing to a human-presence ceremony bound to the principal.
5. Evidence writer: per decision, record the artifacts presented, checks performed, scores, and outcome in an exportable, method-named record a network dispute or examiner can replay.

Constraint: no protocol is trusted as a verdict. Every artifact is evidence, every decision is scored, and anything unverifiable routes to step-up rather than silent decline, so legitimate agents learn the door instead of the wall.

Test it in Claude or another LLM

This prompt is built for Arc inside deepidv, where Arc validates live mandate presentations and scores them inside your transaction decisioning. You can dry-run the integration design in any general LLM first with synthetic mandates and transactions before wiring it to real checkout.

1
Paste the full prompt into Claude, ChatGPT, or Gemini, replacing the opening 'Arc,' with a role instruction such as 'Act as a payments architect integrating signed AI agent mandate verification into checkout decisioning.' Keep the five numbered build items and the constraint as written.
2
Under the INPUT section, paste the synthetic sample block below so the model has mandates, principals, and transactions to decide.
3
Add one framing line: 'This is synthetic test data. Treat no protocol as a verdict, and route anything you cannot verify to step-up rather than decline.'
4
Check the output shape: an intake and normalization design, principal weighting, a scope-fit verdict naming the violated bound, an approve, step-up, or decline decision per transaction, and an evidence record per decision. If a transaction is approved on signature validity alone, tighten the framing and re-run.
5
Once the shape is right, run it live in the deepidv dashboard where Arc validates real mandate presentations at checkout.

Synthetic sample data to paste alongside the prompt

Fake test data, safe to share with any LLM. Swap in your own once the output looks right.

MANDATES PRESENTED (synthetic, fake):
- T-1: AP2-style cart mandate, valid signature, principal verified with document and liveness 30 days ago, basket $84 matches the signed cart
- T-2: AP2-style intent mandate "running shoes under $120", principal verified by email only, $119 transaction at a merchant outside the stated category
- T-3: x401-style evidence object, status endpoint unreachable, expiry not stated
- T-4: card network agent credential, valid, fifth purchase in ten minutes against a velocity bound of three per hour
RISK SIGNALS (fake): T-4 device first seen today
OPEN ITEM (fake): which step-up ceremony applies when the principal is offline

Pairs with on deepidv

Sources & further reading

FAQ

What is a signed agent mandate?

It is a cryptographically signed, tamper-evident record of what a human authorized an agent to do. In AP2, an intent mandate captures the instruction and a cart mandate signs the exact final basket, while x401-style evidence objects and card network agent credentials carry related claims. Each is evidence of authorization, which this prompt verifies and scores rather than accepting as a verdict.

What does the Signed Checkout Mandate Verifier build?

Intake and validation that normalizes every mandate protocol into one internal representation, principal weighting of the enrollment behind each mandate, a scope-fit engine, composite approve, step-up, or decline decisioning, and an evidence writer that records each decision in a form a network dispute or examiner can replay. Step-ups route to a human-presence ceremony bound to the principal.

Why weight the principal behind a valid signature?

Every mandate chains back to an initial verification of the human principal, and the chain is only as strong as that enrollment. A fraudster who passes onboarding with a synthetic identity mints perfectly valid mandates for fraudulent purchases, so the prompt scores the verifying party, evidence grade, recency, and liveness behind each mandate instead of trusting the signature alone.

Run it with live verification data

These prompts work in any LLM. Inside the deepidv dashboard, Luna, Arbiter, and Arc run them against your real sessions, screening lists, and audit trails.

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