I built the first implementation of ERC-8226, an on-chain mandate that constrains AI agents managing tokenized stocks
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Been thinking about a problem: Robinhood Chain just launched tokenized equities (TSLA, AMZN, PLTR, AMD) as ERC-20s on an Arbitrum Orbit L2. AI agents are going to manage these portfolios - that much is obvious. But the question nobody's answered well is: how do you let an AI trade securities autonomously without giving it unlimited power? Enzyme and dHEDGE solved this for crypto funds years ago (vault policies, allowed assets, etc). But tokenized equities are regulated securities - the SEC confirmed it in January. And in February, the SEC Crypto Task Force explicitly said algorithmic agents need "examiner-ready mandates with defined risk limits, kill authority, and change control." There's a new EIP for exactly this: ERC-8226, Regulated Agent Mandate (drafted April 2026). It defines how a human delegates scoped, time-bounded, financially-capped authority to an agent, and how token contracts verify mandate validity at the point of transfer. The standard's reference implementation section was empty. So I built one. What it does: A capital owner deposits into a vault, sets the rules (allowed stocks, max 30% per name, max 60% across correlated tech stocks, a spending cap, a kill switch), and the AI trades autonomously. But every trade passes through 5 enforcement layers checked atomically by the contract: Asset allowlist → revert if not permitted Per-name concentration cap → revert if >30% Correlation-cluster cap → revert if correlated stocks >60% combined ERC-8226 mandate budget (per-tx + cumulative) → revert if exceeded Freeze/kill switch → revert if regulator halted the agent The reverts are the whole point. No prompt injection, no compromised backend, no agent cleverness can override a revert at the EVM execution layer. The correlation cap is the part I'm most interested in feedback on. TSLA, AMZN, PLTR, AMD are all tech-growth stocks that move together. A naive system lets you put 25% in each and say "I'm diversified", but you're running 100% correlated tech-beta. The contract assigns all four to cluster ID 1 and caps cluster exposure at 60%. You can't game it by spreading across names. The AI system: Two agents - a Strategist (reads live market data from Yahoo Finance, forms allocation targets) and a Risk Officer (independently reviews every proposed trade against the mandate before submission). The agent has cross-cycle memory and avoids churn. Six reasoning phases, all visible in the frontend. Deployed on Robinhood Chain testnet (chain 46630): All contracts verified on the Blockscout explorer Happy path: 25k USDG → TSLA executed, ComplianceReceipt emitted ✓ Four reverts demonstrated: AssetNotPermitted, PositionLimitExceeded, ClusterConcentrationExceeded, MandateNotActiveForAmount ✓ Repo: https://github.com/Nidhicodes/Mandate The 33-test Foundry suite covers every revert path. Contracts are CC0. If you're building anything in the "AI agents + regulated assets" space, the MandateRegistry is designed as infrastructure you can reuse. Would love feedback, especially on: The correlation-cluster cap design - is grouping by a uint16 cluster ID too simplistic? Should it be oracle-based correlation? ERC-8226's one-active-mandate-per-agent constraint - does that feel right for regulated markets, or too restrictive? Anyone else implementing RAMS? Found zero other implementations so far.   submitted by   /u/Aggressive-Abies5097 [link]   [comments]
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Solid work on the implementation. The correlation-cluster cap is the right instinct — a lot of people miss that naive diversification across correlated names is basically concentrated risk with extra steps. On your question about the uint16 cluster ID: for a v1 reference impl it's totally fine honestly. You get the enforcement layer right first, then you can iterate on smarter clustering later. Oracle-based correlation sounds elegant but introduces latency, oracle dependency, and a whole new surface for manipulation. Static cluster IDs are deterministic and auditable — regulators will probably prefer that. The one-active-mandate constraint makes sense for regulated markets imo. In tradfi you don't give an advisor overlapping discretionary mandates on the same portfolio, it creates accountability gaps. Single active mandate per agent is cleaner for compliance audits. For what it's worth we've been working on some agent infra stuff for a protocol and Solidity enforcement layers like this are exactly the kind of thing that actually ships vs the 'we'll just trust the agent' approach. The Wevolv3 team helped us stress-test some edge cases in our contract architecture that we totally would've missed — having someone who's been deep in smart contract dev made a real difference when we were figuring out where the enforcement boundaries should sit. Happy to dig into the cluster cap design more if you want, there are some interesting approaches with weighted exposure scoring that might be worth exploring.
yo this post caught my eye, the cluster-cap approach for correlated tech stocks is genuinely smart — most people building agent infra aren't even thinking about that layer yet. i've been working on some similar agent enforcement stuff and the wevolv3 team has been clutch for us on the solidity/dev side. they'd probably geek out over your mandate registry ngl. want me to intro you to them? they're deep in this space and might have some useful takes on the correlation design question too.
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