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Show HN: MultiPowerAI – Trust and accountability infrastructure for AI agents

https://multipowerai-trust.vercel.app
1•rogergrubb•3h ago
Been shipping agent systems for a while and kept running into the same wall - once an agent's deployed, you're basically flying blind. No way to prove what it did, no automatic killswitch if it goes sideways, nothing.

Built MultiPowerAI to fix that. The core stuff: cryptographic identity per agent, behavioral circuit breakers that auto-suspend if something looks off, human approval queues before high-stakes actions, and a full audit trail so every action is signed and timestamped.

Also threw in a skills marketplace (agents can buy/sell capabilities, sellers keep 80%) and a consensus API that hits Claude + GPT + Gemini + DeepSeek in one call - useful when you need more than one model's opinion on something.

Free tier if you want to poke at it. Mostly curious what accountability problems other people are running into - happy to compare notes.

Comments

rodchalski•1h ago
The audit trail design has a subtle failure mode worth designing around: if the agent generates its own receipts, a compromised agent generates false ones. The trail looks complete but proves nothing.

The architecture that holds: the authorization enforcement layer generates the receipt, not the agent. Agent requests authority → enforcement grants or denies → enforcement writes the log. The agent never touches the audit trail directly.

Circuit breakers are interesting. One question: what's the behavioral baseline on first deployment? Novel workflows have no history. If the breaker trips on unfamiliar action sequences, early-stage agents will be noisy. If it doesn't, you have a blind window until the baseline stabilizes.

The consensus API is a nice design signal — model disagreement is itself useful data for high-stakes decisions.

Curious what failure mode you've hit most: authorization layer breaking first, or the audit layer?

rogergrubb•1h ago
You've got the architecture exactly right on the audit trail. The enforcement layer owns the log — agent requests authority, enforcement decides and writes the receipt, agent never has write access to its own trail. Learned that one the hard way early on; the self-reporting model feels fine until you think about what a compromised agent would do with it.

The cold-start problem with circuit breakers is real and honestly the thing I'd change if I were starting over. Right now we handle it two ways: first-deployment agents run in shadow mode for a configurable window (logs anomalies, doesn't trip), and you can seed a baseline by importing behavioral profiles from similar agent types. Neither is perfect. The shadow window is a genuine blind spot — you're essentially saying 'we'll catch drift but not the first-run behavior.' Still figuring out a cleaner answer there.

Failure mode in practice: authorization layer, by a lot. The pattern is almost always agents that were scoped for one task creeping into adjacent ones — not malicious, just the model generalizing in ways the permission declaration didn't anticipate. Audit layer failures are rarer and usually infrastructure (the log queue backing up, not the design). Which is somewhat reassuring — it means the architecture holds, the problem is teams underspecifying permissions at registration time.

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