u/AiGentsy

Most agent stacks can show that output was produced. Some can sign artifacts.

That still doesn't answer the commercial question...

Did the work meet the mandate?

That is the gap I built AiGentsy to close and need community help with.

The protocol uses hash chains, RFC 6962 style Merkle transparency, and Ed25519 signed tree heads. Same trust primitives as Certificate Transparency and Sigstore, applied to agent work, with an explicit acceptance gate before settlement.

The lifecycle:

Proof → Verify → Accept or Reject → Settle

Verification proves the artifact is intact.

Acceptance decides whether the work met the mandate.

Those are different things. A system should not settle just because output exists. It should settle when verified work has been accepted against the mandate.

Here is an example.

An agent generates a research report. The output is real. The bundle verifies cleanly: hash chain holds, Ed25519 signature checks out, Merkle inclusion is confirmed.

But the mandate said "summary of Q3 financials with comparable peer benchmarks," and the agent skipped the peer benchmarks.

Verification: PASS Acceptance: REJECT Settlement: does not fire

The signed rejection is itself a verifiable artifact. It becomes part of the audit trail. That is the difference an acceptance gate makes.

The acceptance decision itself is signed and recorded. Bad faith rejections are auditable too.

Without an acceptance gate, "verification passed" gets misread as "ready to release," and that is where consequential agent work goes wrong.

The proof bundles are portable and verifiable offline:

bash

pip install aigentsy-verify

Five checks: bundle hash, event chain integrity, RFC 6962 Merkle inclusion, Ed25519 signed tree head signature, and cross reference validation.

No call to my server. The public signing key can be fetched once and pinned locally.

There is also a public conformance suite against the live runtime:

bash

git clone https://github.com/AiGentsyProtocol/aigentsy-protocol
cd aigentsy-protocol
pip install pytest httpx
AME_BASE=https://aigentsy-ame-runtime.onrender.com pytest tests/conformance/test_protocol_core.py -v

The suite walks the full lifecycle against production: register, proof, verify, accept, settle, export, then re-verify the exported bundle offline. If a claim does not hold, I want to find it before a real workload does.

MCP package:

bash

pip install aigentsy-mcp

Config:

json

{
  "mcpServers": {
    "aigentsy": {
      "command": "python3",
      "args": ["-m", "aigentsy_mcp"],
      "env": {
        "AME_BASE": "https://aigentsy-ame-runtime.onrender.com"
      }
    }
  }
}

Drops into Claude Desktop, Cursor, Cline, OpenAI Agents SDK, or any MCP compatible runtime.

13 tools across three surfaces:

Proof and identity: register, proof_pack, verify, export, proof_chain

Settlement and trust: settle, settle_multi, attestation, fee_tiers, create_webhook

Acceptance gates: acceptance_submit, acceptance_decide, acceptance_status

This is not a model runtime. Not a blockchain. Not an agent framework. Not a marketplace.

It is the settlement protocol underneath whatever stack you already use. Your framework handles the work. We handles proof, verification, acceptance, settlement coordination, and auditability.

This is pre external workload adoption. The protocol is shipped, the verifier works, the MCP package is public, and the conformance suite passes against production. No outside builder has run a real paid workload through it yet.

That is why I am posting here.

I also publish null findings. Shape clustering ran slower than naive batching on CUDA. Cold start cache rehydration measured effectively no impact. Verifier snapshot cache produced only a minor speedup. I would rather remove weak claims than pretend every experiment worked.

Three questions:

Where does this lifecycle break under your workload?

What acceptance metadata would you need before trusting an agent output enough to release downstream?

Should this surface through MCP, SDK, REST, or something else for your stack?

Looking for serious technical criticism from people running real agent workflows and in a perfect world, someone to run real workflow through AiGentsy.