AI agents can now call APIs, execute workflows, and move money.

But there’s no standardized way to generate machine-verifiable proof that an action actually occurred.

Today, most systems rely on: - Logs in a database - Screenshots - Internal audit trails

Those don’t travel well between systems.

I built a small API experiment that issues cryptographically sealed execution receipts.

Under the hood: - The payload is canonicalized - Hashed (SHA-256) - Sealed with HMAC - Timestamped - Stored idempotently

Two endpoints:

POST /execute - Accepts structured JSON - Returns a receipt ID + HMAC seal

GET /verify - Recomputes the hash + verifies integrity

This week I recorded a 90-second demo: An agent “hires” a freelancer, verifies the deliverable, and generates a tamper-evident receipt.

https://www.loom.com/share/845adcf05d2e40c6b495e3b9663fcfd0

Curious about feedback from engineers building: - autonomous agents - workflow systems - distributed automation

Does "proof-of-execution" feel like a real primitive to you? Or is this just glorified logging with extra steps?

Would especially appreciate thoughts on the trust model.

Live at proofrelay.app