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This article introduces Orchanet, a decentralized multi-agent AI orchestration protocol built on the 0G Network. It addresses the black-box trust issue in AI inference through on-chain computation scheduling, storage proof, and payment settlement.
Section 01
This article introduces Orchanet, a decentralized multi-agent AI orchestration protocol built on the 0G Network. It addresses the black-box trust issue in AI inference through on-chain computation scheduling, storage proof, and payment settlement.
Section 02
Currently, the DeFi and Web3 ecosystems are increasingly relying on AI for market analysis, strategy formulation, and decision support. However, all existing AI integration solutions share a fatal flaw— the inability to verify the inference process.
Specifically, the following scenarios are completely indistinguishable to users:
This "black-box problem" constitutes a fundamental contradiction in trust-minimized ecosystems that rely on cryptographic guarantees. If AI outputs cannot be verified, then the automated execution based on smart contracts is at risk of manipulation.
Section 03
Orchanet treats each AI inference task as a first-class on-chain event, anchoring it at every layer of the pipeline via the 0G Network's infrastructure—from computation scheduling and fee settlement to tamper-proof storage traceability and on-chain agent identity verification.
Section 04
Trustless: No centralized servers; computation tasks are scheduled to run on 0G compute provider nodes.
Verifiable: Every output, debate, and consensus score is permanently stored on 0G Storage with an attached verifiable rootHash.
Accountable: Agent identities are registered on-chain, and fee settlement is completed using OG tokens via the 0G Ledger.
Auditable: Any user can retrieve their operation records from 0G Storage at any time using the rootHash.
Section 05
Orchanet orchestrates a committee composed of multiple professional AI agents, including:
These agents process user query prompts in parallel, reach consensus through debate, and seal the entire inference process on the 0G Network.
Section 06
Orchanet's intelligence layer is built on top of the 0G Compute SDK. Unlike routing LLM calls to centralized API endpoints, every inference task in Orchanet's pipeline is distributed to decentralized provider nodes via the 0G Compute SDK.
The 0G Compute Network adopts a performance-based quality model: provider nodes compete to serve inference requests, and the protocol's economic incentives ensure that high-quality, low-latency responses are prioritized.
Parallel Inference Execution: Orchestrators use Promise.all to dispatch agent tasks in parallel while saturating multiple 0G compute provider nodes. This means a three-agent committee (tokenomics modeler + smart contract auditor + critic) can run their respective inferences concurrently, significantly reducing total pipeline latency while ensuring each agent's output is computed on an independent provider node—eliminating correlated failure risks.
Fine-Grained Token Tracking: Every token consumed by each agent is tracked at a fine granularity, and this data is directly fed into the 0G Ledger settlement process to ensure fair economic accounting for each inference call.
Section 07
The 0G Settlement Ledger makes decentralized computing economically sustainable. Each inference request distributed to a 0G provider node incurs a cost denominated in OG tokens, which is settled on-chain via the ledger contract. Orchanet's orchestrator automatically manages this settlement process.
Automatic Fund Replenishment Logic: Before each pipeline run, the orchestrator performs the following steps:
This ensures the pipeline does not stall mid-inference due to insufficient ledger funds—a critical yet subtle engineering requirement for production-grade decentralized computing.
Section 08
The complete record of each pipeline run (including prompts, agent outputs, consensus scores, and metadata) is submitted to 0G Storage. Users can retrieve these records at any time using the rootHash, enabling full audit transparency.
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