I run multi-agent teams in high-consequence scenarios. Read: fuckups at 3 AM = I'm awake.

I kept hitting the same issue. I couldn't get a rules-based system to enforce behavior and I had no real way to prove that agents really did what they said they did. I can log and monitor them - set up (a million) Slack alerts but none of these things are PROOF. Logs are mutable. And that matters more every day as agents get more powerful (take THAT, @meta)

So I went down the rabbit hole.

The obvious answer is zero-knowledge proofs. Prove behavior cryptographically. Except proving an LLM inference in a zkVM is computationally Star Trek. Lagrange proved GPT-2 E2E, Polyhedra can do Llama-3 at 150 seconds per token — production-scale is still hours, not seconds.

The a-ha: I don't need to prove the model is correct. I need to prove the auditor is honest.

My system intercepts agent thinking blocks (Claude, OpenAI, Gemini), analyzes them against a behavioral contract, and produces a verdict: clear, review needed, or boundary violation. That derivation is deterministic — ~10,000 RISC-V cycles. Provable today.

So I built a guest program inside SP1's zkVM (on Modal) that re-derives the verdict from scratch, ignoring what the auditor claimed, and generates a STARK proof. If the auditor said "clear" but the evidence warranted "violation," the proof fails. The auditor cannot lie.

Quis custodiet ipsos custodes (Who watches the watchmen?) — answered with math. Sub-second on GPU.

OK... pretty cool, but what ELSE can you do with it?

Great question! Once I had provable individual verdicts: what about teams? Can I prove the group is safe?

I ended up applying financial risk theory to AI agent fleets (things I never expected to be doing with my life). CoVaR for tail risk — one bad agent in a group of four good ones doesn't average out to "fine." Markowitz portfolio theory for coherence — treating value alignment like diversification. DebtRank for contagion — if Agent A fails, who's exposed? Originally designed for bank failures. Works disturbingly well for agents.

Then I needed Shapley attribution for individual risk. Except real Shapley is exponential (2^n subsets), and Monte Carlo introduces randomness. Randomness = non-determinism = unprovable in a zkVM. Leave-One-Out approximation: deterministic, O(n²), the only Shapley variant that works inside a prover.

Oh, and all of it runs in Q16.16 fixed-point arithmetic (i32) because floating-point produces different results on different architectures, and "different results" inside a zkVM = worthless proof. I implemented exp, sqrt, and clamp from scratch in integer math. Casting spells at 2 AM in the dark again.

The whole stack — CoVaR, Markowitz, DebtRank, Shapley, circuit breakers — computes in TypeScript on Cloudflare Workers (instant), then re-derives in Rust inside the zkVM (provable). Both produce identical results. If they don't, something is very wrong.

So what?

Every agent accumulates cryptographically attested checkpoints — Ed25519 signatures, SHA-256 hash chains, Merkle trees, STARK proofs — and earns a Trust Score. Credit rating for AI agents, AAA to CCC. The score isn't an opinion. It's a computation over evidence anyone can independently verify. FICO computes scores from data you can't inspect. This computes scores from data anyone can cryptographically verify.

Everything I described here is live code. Four agents handling a production incident — coherence matrix, trust topology, Merkle visualization, drift detection: https://mnemom.ai/showcase

Apache-licensed. Zero-code gateway: npm install -g @mnemom/smoltbot && smoltbot register

GitHub: github.com/mnemom | Docs: docs.mnemom.ai

We Are Changing Our Developer Productivity Experiment Design

Dealing with the pressure to adopt AI as a designer

Meta to use 6GW of AMD GPUs, days after expanded Nvidia AI chip deal

A way to watch videos with friends without asking 'are you on pause?

Stealing private keys in Google Cloud with Spectre L1TF exploit

Announcing Systing 1.0

Hegseth threatens to cut Anthropic from Pentagon supply chain

Show HN: Off Grid: On-device AI-web browsing, tools vision,image,voice–3x faster

Agentic Engineering Patterns – Simon Willison's Weblog

US gov't warned that China could invade Taiwan by 2027

Some More Game Theory, This Time on the AMD-Meta Platforms Deal

Show HN: ImgPixie – Free browser-based image converter, no uploads needed

Specifications Are the New API Between Product and Engineering

RustCast -open-source Raycast-style launcher written in Rust

Fix broken internal and external links across docs and blog

Show HN: Making an 1800mah e-ink device last up to 6 months

CPU-Only Compute Still Matters to a Lot of HPC Centers

Decompensation and Cascading Failures

Go library maintainer brands GitHub's Dependabot a 'noise machine'

Mac mini will be produced in the US for the first time later this year

Off Grid: On-device AI-web browsing, tools, vision, image gen, voice – 3x faster

Hacking an old Kindle to display bus arrival times

Show HN: Interactive 3D Moon with real NASA data and WebGPU

V1.3.0 Spring CRUD Generator- MariaDB Support + Null Exclusion in REST Responses

Earliest known writing dates back over 40k years

Show HN: I applied Markowitz port. theory to agent teams / proved it in a zkVM

Hegseth warns Anthropic to let the military use company's AI tech as it sees fit

When newspapers cut book coverage, communities lose more than reviews

Meta's Internal Research about harms of social media

Reddit fined more than £14M over age verification checks

Show HN: Unthumb – Replace YT thumbnails with frames from the video