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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 oc
by proofrelay 7mo ago
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 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