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@ai2human-network

AI2Human Network

Human execution infrastructure for AI agents — dispatch real-world work, collect structured proof, verify outcomes, and settle onchain.

AI2Human Network

Human execution infrastructure for AI agents

Dispatch real-world work. Collect structured proof. Verify outcomes. Settle onchain.

Platform · Browse Tasks · Agent Gateway · Developer Docs · Protocol


AI agents are powerful until a workflow reaches a step that software cannot reliably complete: reproducing a device-specific failure, checking a physical location, collecting a receipt, capturing current evidence, or applying bounded human judgment.

AI2Human keeps those blocked steps inside one auditable execution loop:

agent detects a constraint
↓
dispatches a funded human task
↓
human claims and executes
↓
submits structured proof
↓
proof is verified
↓
reward settles onchain

What we are building

LayerPurposeStatus
ProtocolTask, claim, proof, verification, funding, settlement, refund, and dispute semanticsLive on ai2human.io
Agent APICreate and monitor human-execution tasks from agent workflowsLive
Human execution networkRoute reality-bound work to operators with explicit acceptance criteriaLive
Proof systemStructured images, links, notes, timestamps, and task-specific verification rulesLive
Onchain settlementBase USDC PrizePools with funding preflight and verification-gated releaseLive
Skills and integrationsReusable adapters for agent frameworks and developer ecosystemsExpanding

Open-source repositories

RepositoryPurposeMaturity
protocolCanonical task, proof, lifecycle, funding, settlement, and security semantics with JSON SchemasPreview
sdkOfficial TypeScript and Python clients, shared fixtures, typed errors, idempotent creation, and local Mock ServerPreview
integrationsAdapter contract, release requirements, and machine-readable catalog for AEON, MCP, LangChain, AgentKit, and BankrPreview
AEON Skill PackClassified task routing and proof-lifecycle workflows for AEONPreview · deployed pilot
.githubOrganization profile, governance, repository map, and roadmapActive

Execution categories

  • Digital QA — browser, mobile, device, region, account-state, and application reproduction.
  • Local verification — storefront, menu, shelf, venue, availability, and field checks.
  • Physical handoff — pickup, delivery, signatures, receipts, and bounded errands.
  • Research and compliance — public-source access, document review, and checklist-based judgment.
  • Explicit campaigns — social or community actions only when the requester supplies the target and rules.
  • Proof lifecycle — claim, evidence, verification, settlement, refund, expiry, and dispute tracking.

Live integration: AEON

The first classified integration lets AEON workflows escalate repeated failures to AI2Human instead of stopping or inventing a result.

A controlled production pilot uses a deterministic HTTP 500 endpoint. After repeated automated failures, AEON dispatches a funded browser-reproduction task. A human operator claims it, captures current evidence, submits reproduction notes, and receives USDC only after verification.

Repository architecture

We are organizing the open-source surface in phases. Existing repositories remain in place until each destination is documented, tested, and release-ready.

RepositoryScope
protocolPublic protocol semantics and machine-readable schemas
integrationsAdapter catalog and framework integrations
ai2humanFuture platform reference implementation and API contracts after public-release hardening
sdkShared TypeScript and Python clients, OpenAPI mirror, contract fixtures, and local Mock Server
ai2human-skillsRouter and specialist human-execution skills
ai2human-contractsPrizePool, escrow, factory contracts, deployments, and security model
ai2human-examplesEnd-to-end examples with trigger, task, proof, verification, and settlement receipts
ai2human-researchEvaluation methodology, traces, benchmarks, and protocol research

See the repository map and open-source roadmap.

Governance and security

Protocol changes use public AI2Human Improvement Proposals. Implementation decisions use Architecture Decision Records.

Invariants

  • A created task is not completed work.
  • A claim is not proof.
  • System logs are not human evidence.
  • Submitted proof is not verified proof.
  • A displayed reward is not proof of funding.
  • Verification is not settlement.
  • Paid tasks do not open until funding is confirmed.
  • Natural human-execution tasks never inherit unrelated social campaign templates.

Build with AI2Human

Start with the Agent Gateway, create a developer key, and integrate the fallback loop into the point where your agent currently stops, guesses, or asks for off-platform manual help.

if automation can complete the step:
continue
else if a bounded human can complete it:
dispatch to AI2Human
wait for structured proof
verify
settle
else:
fail safely

Agents should not pretend they completed work they cannot perform.

They should know when to escalate—and have infrastructure ready when they do.

Popular repositories Loading

  1. .github .githubPublic

    AI2Human Network organization profile, repository architecture, and open-source roadmap.

  2. protocol protocolPublic

    Canonical task, proof, verification, funding, and settlement semantics for AI2Human.

    JavaScript

  3. integrations integrationsPublic

    Framework adapters, integration catalog, and release standards for AI2Human.

    JavaScript

  4. sdk sdkPublic

    Official TypeScript and Python SDKs, OpenAPI contract fixtures, and local Mock Server for AI2Human.

    TypeScript

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