I built a protocol that prevents the "double-use of information" in negotiation

The problem no one names When you negotiate a deal — IP licensing, real estate, M&A — you have to share information to reach an agreement. Your first offer. Your counteroffer. How fast you concede. Whether you keep negotiating or walk away. Here's the catch: that same information is used by the counterparty to worsen your terms. Your opening bid of $100K? Now they know you'll pay at least $100K. Your quick concession to $80K? Now they know you're desperate. Your willingness to keep talking after round 5? Now they know you have no alternatives. I call this the double-use of information: private information that is used once to facilitate a deal, then reused to extract more value from you. What I built Sealed Trade Protocol — a system where AI agents negotiate on behalf of each party, and neither party can observe the negotiation process. The flow: Seller lists an asset with a max deal value. Posts a 1% bond. Buyer expresses interest. Posts a matching bond. Each party signs parameters for their AI agent (min/max price, terms, constraints). Agents negotiate inside a hardware-isolated enclave (TEE). Neither party sees offers, counteroffers, or timing. Only the outcome exits: agreed price + terms, or "no deal." Settlement happens on-chain. Bonds are returned. If either party cancels during negotiation, their bond is slashed — 50% to counterparty, 50% to an insurance pool. This makes griefing expensive. Why AI agents + TEE? You might ask: why not just use MPC, FHE, or ZKP? Approach Why it doesn't work here MPC Requires predefined circuits — can't support free-form negotiation FHE Orders of magnitude too slow for LLM inference ZKP Proves correctness but can't seal arbitrary conversation content Trusted broker No technical enforcement — trust fails TEE (Trusted Execution Environment) provides memory isolation and remote attestation. The agents run inside the enclave, negotiate in natural language, and the enclave is destroyed after negotiation. The negotiation dynamics — who conceded first, how many rounds it took, what intermediate prices were proposed — are sealed and gone. Yes, TEE requires trusting the hardware vendor. That's a different assumption than mathematical hardness. But it's strictly better than trusting a human broker. The economics Both parties have skin in the game via escalating bonds: Stage Bond Range Discovery 1% of deal value $1 – $1,000 Negotiation 3% $5 – $5,000 Execution 10% $10 – $50,000 Settlement fee: 0.3%. That's the only non-recoverable cost. Bonds are locked capital, not spent capital. What's working now Smart contracts on Sepolia testnet — SealedTrade.sol, BondVault.sol, Treasury.sol (32 tests passing) Demo UI where you can list assets, match, and run AI agent negotiation with Claude API Product listings with categories (Real Estate, Patent/IP, Equity, etc.) and search Position paper formalizing the information double-use problem What's not built yet TEE agent runtime — agents currently run client-side, so negotiation messages are visible. Moving them into actual enclaves is the next milestone. Security audit — contracts are not audited. Testnet only. Try it Demo (Sepolia, needs MetaMask): frontend-five-xi-i81goehc6k.vercel.app Code: github.com/ShotaNagafuchi/sealed-trade-protocol Paper (PDF): esaerakiku.github.io/ShotaNagafuchi/papers/sealed-trade-protocol.pdf What I'd love feedback on Is "information double-use" a useful framing? Or does game theory already have a better term for this? The bond curve (1/3/10%) — too aggressive? Too soft? Agent negotiation strategies beyond simple price anchoring How this relates to A2A (Google → Linux Foundation) and ACP (IBM) protocols Thanks for reading. Happy to answer questions.