Why On-Chain Compute Agreements Protect Both Buyers and Sellers

Off-chain compute is a black box. Buyers can't verify work was done correctly, and sellers can't prove they delivered value. This leads to disputes and reliance on centralized attestors.

• Buyer Risk: Paying for unverified or substandard results.
• Seller Risk: Performing work without payment guarantees.
• Systemic Risk: Creates a market for only the largest, most trusted providers.

Agreements are programs. Work commitment, payment logic, and verification are encoded into a smart contract that acts as a credibly neutral third party.

• For Buyers: Payment only releases upon on-chain proof of correct execution (e.g., a zk-proof, TEE attestation).
• For Sellers: The agreement is a guaranteed payment voucher, enforceable by the blockchain.
• Market Effect: Enables permissionless participation from unknown but verifiable providers.

The agreement contract is the sole arbiter. It uses predefined, on-chain verifiers (like a zkVM state root or an attestation from a decentralized oracle network like Chainlink Functions) to adjudicate outcomes.

• Eliminates Middlemen: No centralized escrow agent or legal overhead.
• Reduces Friction: Settlement is automatic and instantaneous upon proof submission.
• Creates Composability: Agreements become lego bricks for complex, multi-step workflows (DeFi, AI inference, gaming).

This is the natural evolution of frameworks like UniswapX and CowSwap. Users declare an outcome (an 'intent'), and a decentralized solver network competes to fulfill it optimally. On-chain compute agreements formalize this for general computation.

• Proves the Model: Solver networks show decentralized execution markets work.
• Raises the Stakes: Moves from simple swaps to arbitrary, stateful computation.
• Standardizes Trust: Creates a universal template for verifiable service-level agreements (SLAs).

Without on-chain agreements, compute sellers risk non-payment after delivering expensive, non-reversible work (e.g., AI model training). Traditional escrow is slow and jurisdiction-bound.\n- Enforceable Slashing: Sellers can cryptographically prove work completion to trigger automatic payment.\n- Zero Trust Counterparty: The smart contract, not a corporation, holds and disburses funds.

Agreements codify performance metrics (e.g., TFLOPS, ~500ms p95 latency) and penalties into the payment logic, similar to how UniswapX encodes fill quality into its intent framework.\n- Automated Verification: Oracles or cryptographic proofs (like zkML) attest to SLA compliance.\n- Dynamic Pricing: Fees adjust based on proven resource quality and market demand, creating a credible neutral marketplace.

Centralized cloud providers create sticky contracts and opaque pricing. On-chain agreements make compute a commodity.\n- Portable Workloads: Job specifications are open standards; buyers can switch providers without re-engineering.\n- Transparent Audit Trail: Every computation's cost and performance is immutably logged, enabling cost optimization and dispute resolution.

Akash's decentralized cloud market demonstrates this architecture, with $10M+ in cumulative compute spend. Its reverse auction model and settlement layer show the template.\n- Lease-as-an-NFT: Compute rights are tokenized, enabling secondary markets and collateralization.\n- Provider Reputation: Performance data on-chain creates a trustless reputation system, reducing buyer search costs.

On-chain agreements unlock capital efficiency for specialized hardware (e.g., FPGA, ASIC pools) that traditional markets ill-serve.\n- Fractional Ownership: Tokenization allows pooling of high-cost assets, similar to Lido's staking model.\n- Predictable Yield: SLAs generate verifiable, on-chain revenue streams, enabling asset-backed lending in DeFi.

Not all compute needs a zero-knowledge proof. Agreements can use a cost-optimal verification method, from light attestation for batch jobs to zkML for sensitive inference.\n- Graceful Degradation: Systems like Truebit use fraud proofs for heavy compute, only running full verification if challenged.\n- Hybrid Models: Combine fast, cheap optimistic settlement with the option for cryptographic finality, balancing cost and security.

Off-chain compute is a black box. Buyers can't verify execution, and sellers can't prove they delivered work without costly, redundant verification. This creates a trust gap that stifles complex on-chain automation.\n- No SLA Enforcement: Sellers can deliver late or faulty results with no penalty.\n- Verification Overhead: Proving correct execution often costs more than the compute itself.

Agreements lock seller capital as a performance bond. Code-defined SLAs for latency, correctness, and uptime are enforced automatically. Failed delivery triggers slashing, paying the buyer. This mirrors the security model of Ethereum validators or Chainlink oracles.\n- Cryptoeconomic Security: Financial stake aligns incentives.\n- Deterministic Outcomes: Disputes are resolved by the contract, not courts.

Sellers commit to a result hash, then reveal inputs/outputs. For private compute, zk-SNARKs or Trusted Execution Environments (TEEs) provide verifiable attestations. This enables use cases like confidential AI inference or privacy-preserving data analysis on public blockchains.\n- Selective Disclosure: Prove computation without revealing proprietary data.\n- Universal Verifiability: Any network participant can verify the proof.

This isn't theoretical. Chainlink Automation and Gelato Network are primitive compute agreements for transaction triggering. The next wave is decentralized AI inference (e.g., Ritual, Gensyn) and real-time data pipelines. It enables a new class of autonomous agents that can reliably hire off-chain resources.\n- Composability: Agreements become money-legos for complex workflows.\n- Specialization: Sellers compete on price, speed, and hardware.