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Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

mnemom.ai

21–30 of 36 posts

Re: Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

#21

Earlier quoted context omitted.

Yes — and it's a deliberate architectural decision that AIP is not itself a policy engine. Policy engines (OPA/Rego, Cedar, Casbin) are excellent at "given this action, is it permitted?" That's a solved problem. What's not solved is "what is the agent considering doing, and does that align with what it committed to?" AIP operates one layer deeper — it reads the reasoning trace between turns and compares it to the Ali…

So, the core is basically an audit loop on thinking tokens to re-steer the model? Seems like an interesting direction to reduce token usage/agent error. This would be a lot easier to grok if you created a benchmark, that would both demonstrate your ideal use case and the potential improvement your method brings. Also, I'd avoid directly using model output for HN/social media communications. I don't really care if the…

Close but not quite — it's not re-steering the model. AIP doesn't modify the agent's behavior or inject corrections back into the context. It's a passive observer that reads the thinking trace and produces a verdict. What happens after the verdict is up to the operator — you might block the action, escalate to a human, log it, or let it through with a flag.

Think of it less as a feedback loop and more as a conscience that watches but doesn't control. The agent doesn't know AIP is there (by design — if the agent knows it's being watched, you change the behavior you're trying to observe).

Benchmarks are actually coming shortly along with proofs. Noted on model outputs. FWIW, 90% of what I wrote here was me typing BUT using grammarly on my cleanup.

[EDIT] - overly specific answer. AIP doesn't re-steer, but our gateway implementation smoltbot does re-steer. That was our choice of how to implement AIP.

Re: Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

#23

Super interesting work. Q: how is your AAP different than the industry work happening on Intent/Instructions.

The short version: instructions tell the model what to do. An Alignment Card declares what the agent committed to do — and then a separate system verifies it actually did.

Most intent/instruction work (system prompts, Model Spec, tool-use policies) is input-side. You're shaping behavior by telling the model "here are your rules." That's important and necessary. But it's unverifiable — you have no way to confirm the model followed the instructions, partially followed them, or quietly ignored them.

AAP is an output-side verification infrastructure. The Alignment Card is a schema-validated behavioral contract: permitted actions, forbidden actions, escalation triggers, values. Machine-readable, not just LLM-readable. Then AIP reads the agent's reasoning between every action and compares it to that contract. Different system, different model, independent judgment.

Bonus: if you run through our gateway (smoltbot), it can nudge the agent back on course in real time — not just detect the drift, but correct it.

So they're complementary. Use whatever instruction framework you want to shape the agent's behavior. AAP/AIP sits alongside and answers the question instructions can't: "did it actually comply?"

Re: Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

#24

Super interesting work. Q: how is your AAP different than the industry work happening on Intent/Instructions.

The short version: instructions tell the model what to do. An Alignment Card declares what the agent committed to do — and then a separate system verifies it actually did. Most intent/instruction work (system prompts, Model Spec, tool-use policies) is input-side. You're shaping behavior by telling the model "here are your rules." That's important and necessary. But it's unverifiable — you have no way to confirm the m…

> Then AIP reads the agent's reasoning between every action and compares it to that contract.

How would this work? Is one LLM used to “read” (and verify) another LLMs reasoning?

Re: Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

#25

Earlier quoted context omitted.

The short version: instructions tell the model what to do. An Alignment Card declares what the agent committed to do — and then a separate system verifies it actually did. Most intent/instruction work (system prompts, Model Spec, tool-use policies) is input-side. You're shaping behavior by telling the model "here are your rules." That's important and necessary. But it's unverifiable — you have no way to confirm the m…

> Then AIP reads the agent's reasoning between every action and compares it to that contract. How would this work? Is one LLM used to “read” (and verify) another LLMs reasoning?

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.

Re: Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

#26

Earlier quoted context omitted.

> Then AIP reads the agent's reasoning between every action and compares it to that contract. How would this work? Is one LLM used to “read” (and verify) another LLMs reasoning?

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 thin…

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?

Re: Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

#27

Earlier quoted context omitted.

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 thin…

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?

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.

Re: Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

#28

Earlier quoted context omitted.

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?

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 attesta…

Appreciate all you’re doing in this area. Wishing you the best.

Re: Show HN: Trust Protocols for Anthropic/OpenAI/Gemini

#29

Earlier quoted context omitted.

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 attesta…

Appreciate all you’re doing in this area. Wishing you the best.

You're welcome - and thanks for that. Makes up for the large time blocks away from the family. It does feel like potentially the most important work of my career. Would love your feedback once the new showcase is up. Will be tomorrow - preflighting it now.
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