[alexgarden]

Yep... fair question.

So AIP and AAP are protocols. You can implement them in a variety of ways.

They're implemented on our infrastructure via smoltbot, which is a hosted (or self-hosted) gateway that proxies LLM calls.

For AAP it's a sidecar observer running on a schedule. Zero drag on the model performance.

For AIP, it's an inline conscience observer and a nudge-based enforcement step that monitors the agent's thinking blocks. ~1 second latency penalty - worth it when you must have trust.

For both, they use Haiku-class models for intent summarization; actual verification is via the protocols.

[tiffanyh]

Dumb question: don’t you eventually need a way to monitor the monitoring agent?

If a second LLM is supposed to verify the primary agent’s intent/instructions, how do we know that verifier is actually doing what it was told to do?

[alexgarden]

Not a dumb question — it's the right one. "Who watches the watchmen" has been on my mind from the start of this.

Today the answer is two layers:

The integrity check isn't an LLM deciding if it "feels" like the agent behaved. An LLM does the analysis, but the verdict comes from checkIntegrity() — deterministic rule evaluation against the Alignment Card. The rules are code, not prompts. Auditable.

Cryptographic attestation. Every integrity check produces a signed certificate: SHA-256 input commitments, Ed25519 signature, tamper-evident hash chain, Merkle inclusion proof. Modify or delete a verdict after the fact, and the math breaks.

Tomorrow I'm shipping interactive visualizations for all of this — certificate explorer, hash chain with tamper simulation, Merkle tree with inclusion proof highlighting, and a live verification demo that runs Ed25519 verification in your browser. You'll be able to see and verify the cryptography yourself at mnemom.ai/showcase.

And I'm close to shipping a third layer that removes the need to trust the verifier entirely. Think: mathematically proving the verdict was honestly derived, not just signed. Stay tuned.