### Terms and Conditions
- [x] I agree to the [Grant Agreement](https://9ba4718…c-5c73-47c3-a024-4fc4e5278803.usrfiles.com/ugd/9ba471_f81ef4e4b5f040038350270590eb2e42.pdf) terms if funded
- [x] I agree to [Provide KYC information](https://9ba4718c-5c73-47c3-a024-4fc4e5278803.usrfiles.com/ugd/9ba471_7d9e73d16b584a61bae92282b208efc4.pdf) if funded above $50,000 USD
- [x] I agree to disclose conflicts of interest
- [x] I agree to adhere to the [Code of Conduct](https://forum.zcashcommunity.com/t/zcg-code-of-conduct/41787) and [Communication Guidelines](https://forum.zcashcommunity.com/t/zcg-communication-guidelines/44284)
- [x] I understand all milestone deliverables will be validated and accepted by their intended users or their representatives, who will confirm that the deliverables meet the required quality, functionality, and usability for each user story.
- [x] I agree that for any new open-source software, I will create a `CONTRIBUTING.md` file that reflects the high standards of Zcash development, using the [`librustzcash` style guides](https://github.com/zcash/librustzcash/blob/main/CONTRIBUTING.md#styleguides) as a primary reference.
- [x] I understand when contributing to existing Zcash code, I am required to adhere to the project specific contribution guidelines, paying close attention to any [merge](https://github.com/zcash/librustzcash/blob/main/CONTRIBUTING.md#merge-workflow), [branch](https://github.com/zcash/librustzcash/blob/main/CONTRIBUTING.md#branch-history), [pull request](https://github.com/zcash/librustzcash/blob/main/CONTRIBUTING.md#pull-request-review), and [commit](https://github.com/zcash/librustzcash/blob/main/CONTRIBUTING.md#commit-messages) guidelines as exemplified in the `librustzcash` repository.
- [x] I agree to post request details on the [Community Forum](https://forum.zcashcommunity.com/c/grants/33)
- [x] I understand it is my responsibility to post a link to this issue on the [Zcash Community Forums](https://forum.zcashcommunity.com/c/grants/33) after this application has been submitted so the community can give input. I understand this is required in order for ZCG to discuss and vote on this grant application.
### Application Owners (@Octocat, @Octocat1)
@nelsonjingusc
### Organization Name
GioroX AI, Inc.
### How did you learn about Zcash Community Grants
Through research on privacy-preserving payment infrastructure while developing AI agent systems. Discovered ZCG via Zcash Community Forum discussions on DeFi integration and developer tools.
### Requested Grant Amount (USD)
$50,000
### Category
Infrastructure
### Project Lead
```project-lead.yaml
Name: Nan (Nelson) Jing
Role: Founder & Chief Architect, GioroX AI, Inc.
Background:
- PhD Computer Science, University of Southern California
- 20+ peer-reviewed papers (600+ citations) in blockchain provenance, decentralized systems, and AI engineering
- Bloomberg LP: Senior R&D / Mobile Architecture Lead
→ Apple "Best Finance Application of the Year"
→ Bloomberg Business Innovator Award
- Founding Engineer, Provenance AI, Inc. (under Prof. Yann LeCun, 2016 Turing Award Laureate)
→ Built multimodal AIGC pipelines and AI agent workflows for copyright protection and auditing trails
Responsibilities:
- Overall technical architecture and protocol design
- Zcash SDK integration and privacy layer implementation
- Smart contract development and security
- Team coordination and milestone delivery
```
### Additional Team Members
```team-members.yaml
N/A
Dr. Jing will lead all technical development for this initial phase. Future expansion may include additional engineers for SDK maintenance and developer relations, funded separately.
```
### Project Summary
WAP3 brings Zcash to the emerging AI agent economy by providing a smart contract bridge, escrow infrastructure, and provenance
platform that enables autonomous AI agents to make privacy-preserving payments using ZEC.
### Project Description
**OVERVIEW**
AI agents are rapidly evolving from research prototypes to autonomous economic actors. Major AI labs (OpenAI, Anthropic, Google) are building agent frameworks that can browse the web, purchase services, and execute complex workflows. These agents need payment infrastructure that protects their strategies and competitive advantages.
WAP3 (Web3-based Agent Payment & Provenance Protocol) positions Zcash as the privacy-first payment layer for this emerging AI agent economy.
**WHY THIS MATTERS FOR ZCASH**
The AI Agent Opportunity:
- $10B+ invested in AI agent companies (2024-2025)
- Anthropic's Model Context Protocol (MCP) standardizes agent tools
- OpenAI exploring autonomous agent payments
- Hedge funds deploying AI trading systems
- Enterprise RPA evolving into autonomous agents
Privacy as Competitive Necessity:
When AI agents make payments on public blockchains, they expose:
- Trading strategies (for financial AIs)
- Supplier relationships (for procurement AIs)
- Research directions (for data-buying AIs)
- Business intelligence (for enterprise AIs)
ZEC is the only credible privacy solution, but currently lacks:
- Smart contract layer for agent logic
- Escrow infrastructure for autonomous transactions
- Provenance tracking for compliance
- Developer SDK for agent integration
**STRATEGIC POSITIONING**
WAP3 establishes Zcash as the de facto privacy layer for AI agent payments before this market matures. First-mover advantage in agent infrastructure translates to:
- Increased ZEC transaction volume
- New developer community (AI + Web3)
- Novel use case beyond human users
- Media/marketing opportunities
**TECHNICAL FOUNDATION**
WAP3 leverages:
- Zcash Orchard Protocol for privacy-preserving payments (shielded pool)
- Zcash Shielded Assets (ZSA) for multi-asset programmability
- Off-chain cryptographic escrow using Hash Time-Locked Contracts (HTLC)
- Zcash-native storage for encrypted provenance
- Open standards for maximum interoperability
### Proposed Problem
**PROBLEM 1: AI AGENTS CANNOT USE ZEC**
Current Limitation:
AI agents can integrate with ETH, SOL, and other smart contract chains, but have no pathway to use ZEC for payments.
Why This Matters:
- AI agents need privacy for competitive advantage
- Public blockchain payments expose strategies
- ZEC offers privacy but lacks agent infrastructure
Evidence:
- No existing ZEC payment SDK for AI frameworks
- No escrow system for autonomous agent transactions
- No provenance layer for agent payment auditing
**PROBLEM 2: ZEC LACKS PROGRAMMABILITY FOR COMPLEX PAYMENTS**
Current Limitation:
ZEC supports basic send/receive but cannot execute:
- Conditional payments (release on proof verification)
- Multi-party settlements (split payments to multiple addresses)
- Time-locked escrow (automated fund release)
- Recurring subscriptions (agent service payments)
Why This Matters:
- AI agents require programmable payment logic
- Simple transfers insufficient for autonomous workflows
- Smart contracts needed without sacrificing privacy
**PROBLEM 3: NO PRIVACY-FIRST AGENT PAYMENT STANDARD**
Market Gap:
Existing agent payment solutions use transparent chains:
- Ethereum: All transactions public
- Solana: No privacy layer
- No standard for privacy-preserving agent payments
Opportunity:
ZEC can OWN privacy-first agent payments, but needs infrastructure NOW before market consolidates around transparent alternatives.
**EVIDENCE FROM ZCASH COMMUNITY**
Forum discussions confirm:
- "ZEC needs better cross-chain DeFi access"
- "Developer tools are insufficient"
- "Limited utility beyond basic transfers"
- "Need programmable payment infrastructure"
WAP3 directly addresses these ecosystem needs through AI agent use case.
### Proposed Solution
**SOLUTION OVERVIEW**
WAP3 is a three-layer infrastructure stack that bridges AI agents to Zcash's privacy-preserving payment network:
**LAYER 1: AI AGENT INTEGRATION SDK**
Deliverable: TypeScript/Python SDK for AI frameworks
Features:
- Simple API for ZEC payments from AI agents
- Shielded address management per agent
- Automatic privacy preservation
- MCP tool integration (Anthropic standard)
- LangChain/LlamaIndex connectors
Example Usage:
import { WAP3Client } from 'wap3-agent-payment-poc'
// AI agent initializes payment capability
const agent = new WAP3Agent({
identity: agentKeyPair,
zcash: { network: 'mainnet' }
})
// Agent makes privacy-preserving payment
await agent.payForService({
provider: '...', // ZEC shielded address
amount: '5 ZEC',
escrow: true,
condition: 'service_delivered'
})
**LAYER 2: CRYPTOGRAPHIC SETTLEMENT LAYER**
Deliverable: Off-chain escrow and settlement protocols
Capabilities:
- Hash Time-Locked Contracts (HTLC): Hold ZEC until conditions met
- Conditional Transfers: Release based on ZK-proof verification
- Multi-Party Settlements: Coordinated splits using ZSA multi-asset transfers
- Time Locks: Scheduled/recurring payments via HTLC patterns
- Privacy Preservation: All logic operates within Zcash shielded pool
Technical Approach:
Instead of external smart contracts, WAP3 uses cryptographic primitives native to Zcash:
- HTLC for escrow (agent locks ZEC with hash preimage release condition)
- ZK-SNARKs for proof-of-delivery verification
- ZSA for multi-asset conditional transfers
- Direct agent-to-agent settlement (no intermediary chain)
**LAYER 3: PROVENANCE & AUDIT LAYER**
Deliverable: Encrypted metadata and proof artifacts (using Zcash transaction memos where applicable)
Features:
- Compliance-ready transaction logs
- Privacy-preserving proofs
- Verifiable payment history
- Encrypted metadata storage
- Selective disclosure for audits**
Use Case:
Enterprise AI agents need audit trails for compliance while maintaining operational privacy. WAP3 provides cryptographic proofs of payment integrity without exposing transaction details.
**HOW THIS HELPS ZCASH ECOSYSTEM**
Direct Benefits:
1. New User Base: AI agents as ZEC transactors
2. Increased Volume: Automated agent payments 24/7
3. Developer Attention: AI/ML community discovers ZEC
4. Use Case Innovation: Beyond human-initiated transfers
5. Competitive Moat: First-mover in agent privacy payments
Ecosystem Growth:
- Open-source SDK → Anyone can build agent+ZEC apps
- Platform approach → Useful beyond AI agents
- Educational content → Onboards AI developers to Zcash
- Reference implementation → Template for future projects
**SUCCESS DEFINITION**
6-Month Target Outcomes:
- 30+ AI agents using WAP3
- 3+ AI frameworks integrated (MCP, LangChain, AutoGPT)
- 500+ ZEC transactions from autonomous agents
- 5+ developers building agent+ZEC applications
12-Month Vision:
- 100+ AI agents on platform
- 10+ AI frameworks supported
- 2,000+ ZEC transactions
- 3+ enterprise AI deployments
- ZEC recognized as "privacy layer for AI payments"
### Solution Format
**DELIVERABLES**
**Software Components:**
1. WAP3 TypeScript SDK (npm package)
• AI agent ZEC payment integration
• Shielded address management
• Escrow/settlement APIs
• Full documentation + examples
2. WAP3 Python SDK (PyPI package)
• Python AI framework support
• Same features as TypeScript version
• Jupyter notebook tutorials
3. Cryptographic Escrow Protocols
• HTLC-based escrow implementation
• ZK-proof verification logic
• Multi-signature coordination (optional)
• Open-source, audited
4. Zcash-Native Provenance Layer
• Memo-based encrypted audit trails
• Viewing key selective disclosure
• Compliance reporting tools using Zcash transparency features
5. Developer Documentation
• Quickstart guides
• API reference
• Integration examples
• Security best practices
**OPEN SOURCE**
All code will be MIT licensed, public GitHub repositories.
**COMMUNITY ENGAGEMENT**
- Monthly progress updates on Zcash Forum
- Technical blog posts explaining architecture
- Developer workshops (virtual)
- Demo videos for each milestone
### Dependencies
**TECHNICAL DEPENDENCIES**
Zcash Infrastructure:
- Zcash SDK (Rust/TS) for shielded transactions
- Access to Zcash mainnet/testnet
- Lightwalletd for lightweight client support
Zcash Protocol Dependencies:
- Zcash Orchard Protocol
- ZSA (Zcash Shielded Assets)
- Zcash RPC interface for transaction construction
- Testnet/mainnet access
Storage Layer:
- Zcash transaction memos for encrypted metadata
- IPFS for documentation hosting (public docs only)
Development Tools:
- TypeScript/Node.js ecosystem
- Python 3.10+ environment
- Rust toolchain for Zcash integration tooling and reference implementations
**COLLABORATION NEEDS**
Zcash Foundation/ECC:
- Technical guidance on SDK integration best practices
- Review of privacy preservation approach
- Potential coordination with Zcash Foundation's Developer Relations team
Community:
- Beta testers from AI developer community
- Feedback on SDK design
- Use case validation
**NO UPSTREAM FORK REQUIRED**
WAP3 does not fork existing Zcash repositories. It builds NEW infrastructure (SDK + smart contracts) that INTEGRATES with Zcash via existing public APIs and SDKs.
All Zcash interaction uses official, maintained libraries.
### Technical Approach
**ARCHITECTURE**
**Component 1: AI Agent SDK**
Technology Stack:
- TypeScript (primary): Node.js 18+, TypeScript 5.0+
- Python (secondary): Python 3.10+, FastAPI
- Zcash Integration: zcash-sdk (Rust bindings via WASM/FFI)
Design Principles:
- Privacy-First: All agent transactions use shielded addresses
- Idiot-Proof: Simple API hides complexity
- Framework-Agnostic: Works with any AI agent system
Key Modules:
// Agent identity management
class AgentWallet {
private shieldedAddress: string
private viewingKey: string
async sendPrivatePayment(to: string, amount: string)
async checkBalance(): Promise<Balance>
}
// Escrow management
class EscrowManager {
async createEscrow(params: EscrowParams): Promise<EscrowID>
async releaseEscrow(id: EscrowID, proof: Proof)
async refundEscrow(id: EscrowID)
}
// Provenance tracking
class ProvenanceLogger {
async logPayment(tx: Transaction, metadata: Encrypted)
async generateAuditProof(txID: string): Promise<Proof>
}
**Component 2: Cryptographic Escrow Layer**
Technology: Hash Time-Locked Contracts (HTLC) + ZK-Proofs
Why HTLC Pattern:
- Native to Bitcoin/Zcash architecture (Lightning Network heritage)
- No external dependencies - pure Zcash
- Privacy-preserving by design
- Battle-tested in cross-chain atomic swaps
Escrow Architecture:
// Pseudocode representation
struct ZcashHTLC {
recipient: ShieldedAddress,
amount: ZEC,
hashlock: Hash, // SHA256 of secret preimage
timelock: BlockHeight,
refund_address: ShieldedAddress
}
// Agent creates escrow
Agent → Locks ZEC in HTLC with hash(secret)
↓
Service Provider → Delivers service
↓
Agent → Reveals secret (proof of satisfaction)
↓
Provider → Claims ZEC using secret
↓
OR
↓
Timelock expires → Agent gets refund
All operations use Zcash shielded addresses - fully private.
**Component 3: Privacy Preservation**
Challenge: Maintain ZEC shielding while enabling programmable logic
Approach:
1. On-Chain: Only encrypted commitments
2. Off-Chain: Full transaction details in shielded pool
3. Proofs: ZK-SNARKs verify without revealing
Flow:
Agent wants to pay 5 ZEC for API access
↓
1. Agent creates HTLC transaction (shielded ZEC locked)
2. HTLC includes hashlock (hash of secret known only to parties)
3. API provider delivers service off-chain
4. Agent reveals secret as proof of satisfaction
5. Provider submits secret to Zcash network
6. ZEC automatically released to provider (shielded transfer)
↓
Result: Fully private (no external chain), trustless escrow
**Component 4: AI Framework Integration**
Targets:
- Anthropic MCP: Official MCP tool for ZEC payments
- LangChain: Custom Chain implementation
- LlamaIndex: Tool connector
- AutoGPT: Payment plugin
MCP Tool Example:
{
"name": "zcash_payment",
"description": "Make privacy-preserving payment with ZEC",
"parameters": {
"recipient": "Shielded ZEC address",
"amount": "Amount in ZEC",
"memo": "Optional encrypted memo"
}
}
**SECURITY CONSIDERATIONS**
Threat Model:
1. Private key theft → Secure enclave storage
2. Man-in-the-middle → TLS + certificate pinning
3. Privacy leakage → Formal verification of shielding
4. Smart contract bugs → Third-party audit before mainnet
Audit Plan:
- Smart contracts: External audit ($5K budget allocated)
- SDK: Internal security review + community pentesting
- Documentation: Security best practices for agent developers
### Upstream Merge Opportunities
**NO DIRECT UPSTREAM MODIFICATIONS**
WAP3 does not fork or modify existing Zcash repositories.
**POTENTIAL FUTURE CONTRIBUTIONS**
While this grant focuses on new infrastructure, insights from WAP3 development may benefit the Zcash ecosystem:
Possible Contributions:
1. Agent-Friendly SDK Patterns
• If our agent integration patterns prove valuable, we could contribute examples to zcash-sdk documentation
• Repository: zcash/librustzcash
• Timeline: Post-launch (month 7-9)
2. Shielded Address Best Practices
• Document privacy-preserving patterns for automated systems
• Share with Developer Relations Engineer
• Format: Blog posts + forum discussions
3. Performance Optimizations
• If we discover bottlenecks in high-frequency agent payments
• Share findings with ECC/ZF engineering teams
Coordination:
• Will join Zcash Community Forum before starting development
• Will coordinate with Zcash Foundation’s Developer Relations team on SDK patterns
• Will share progress in bi-weekly forum updates
No Timeline Dependencies:
WAP3 development does not depend on upstream merges. All work uses existing, stable Zcash APIs and official SDKs.
### Hardware/Software Costs (USD)
$8,000
### Hardware/Software Justification
**CLOUD INFRASTRUCTURE ($5,500)**
• AWS / GCP: Development servers, CI runners, and testnet infrastructure
• 9 months × ~$300/month = $2,700
• deployments (Zcash testnet/mainnet integration, transaction orchestration services, monitoring): $800
• encrypted metadata & proof artifacts storage (content addressing, integrity proofs; public artifacts only where appropriate): $2,000
Subtotal: $5,500
Infrastructure costs are intentionally over-provisioned to ensure reproducible builds,
reliable testnet validation, and long-term availability of public artifacts.
**DEVELOPMENT TOOLS ($1,500)**
• IDE licenses (JetBrains suite): $500
• Smart contract security & static analysis tools (Mythril, Slither): $500
• CI/CD, test automation, and reproducibility tooling: $500
Subtotal: $1,500
**DOMAIN & HOSTING ($1,000)**
• Domain registration & DNS: $300
• Documentation hosting, API references, and public artifacts: $700
Subtotal: $1,000
**TOTAL (Hardware / Software): $8,000**
### Service Costs (USD)
$18,000
### Service Costs Justification
**SMART CONTRACT AUDIT ($8,000)**
• Independent third-party security review of Zcash transaction-based HTLC escrow implementation (scripts, memo handling, key management, and integration code)
• Focus on escrow logic, payment settlement, and cross-component safety
• Required for mainnet-grade reliability and ecosystem trust
Audit scope and budget are aligned with medium-complexity contracts that handle value
transfer and provenance guarantees.
**LEGAL / COMPLIANCE ($5,000)**
• Open-source license compatibility review
• Terms of use and contributor documentation
• Compliance preparation for public SDK release
**DESIGN / UX ($5,000)**
• Developer-focused SDK branding and documentation layout
• API ergonomics and onboarding review
• Documentation site usability improvements
**TOTAL (Service Costs): $18,000**
### Compensation Costs (USD)
$24,000
### Compensation Costs Justification
This compensation supports direct delivery of Zcash-aligned public goods and is tied to concrete milestone outputs, not time-based employment.
**Deliverable-based breakdown:**
• Core protocol architecture & Zcash-aligned design: $6,000
• SDK implementation (TypeScript + Python): $7,000
• Smart contract implementation & testnet validation: $5,500
• Integration testing, documentation, and developer onboarding: $5,500
Compensation reflects the scope and technical depth of the deliverables while remaining conservative relative to comparable ecosystem grants. Significant prior research, infrastructure, and unreimbursed preparation work are contributed by the proposer,
reducing total cost to the Zcash ecosystem.
**TOTAL (Compensation): $24,000**
### Total Budget (USD)
$50,000
### Previous Funding
No
### Previous Funding Details
N/A
### Other Funding Sources
Yes
### Other Funding Sources Details
OTHER FUNDING SOURCES - YES
GioroX AI has received $70,000 in early-stage funding:
- $25,000 SAFE at $5M valuation cap from a Web3-focused fellowship program for worldwide selected builders in crypto payment infrastructure
- $45,000 non-dilutive grant from a well-known venture capital organization
All previous funding is non-exclusive and does not restrict working with the Zcash ecosystem. All ZCG-funded work will be open source under MIT license.
### Implementation Risks
**RISK 1: ZCASH SDK INTEGRATION COMPLEXITY**
- Likelihood: Medium
- Impact: Timeline delays (2-4 weeks)
- Mitigation:
- Start with Zcash SDK study (Week 1-2)
- Coordinate with @pacu (Developer Relations)
- Build minimal POC first, iterate
**RISK 2: SMART CONTRACT SECURITY**
- Likelihood: Low (with audit)
- Impact: Critical (fund loss)
- Mitigation:
- Follow Zcash SDK best practices
- Third-party audit ($8K allocated)
- Staged mainnet deployment (testnet first)
- Bug bounty post-launch
**RISK 3: LOW AI DEVELOPER ADOPTION**
- Likelihood: Medium
- Impact: Success metrics not met
- Mitigation:
- Build for MCP (Anthropic's standard = high adoption)
- Prioritize developer experience
- Create compelling demos/tutorials
- Engage AI developer communities early
### Potential Side Effects
**CONCERN 1: PRIVACY MISCONCEPTIONS**
AI developers may misunderstand Zcash privacy guarantees.
Mitigation:
- Clear documentation on shielded vs transparent
- Educational content on privacy best practices
- Built-in SDK warnings for privacy-reducing actions
**CONCERN 2: REGULATORY SCRUTINY**
AI agents making autonomous payments may attract regulatory attention.
Mitigation:
- Include compliance logging features (optional)
- Emphasize legitimate use cases (enterprise, research)
- Provide audit trail capabilities
- Legal review of documentation
**CONCERN 3: NETWORK SPAM**
High-frequency agent payments could stress Zcash network.
Mitigation:
- Implement rate limiting in SDK
- Batch small payments where possible
- Monitor network impact, adjust if needed
- Coordinate with Zcash Foundation on scaling
**POSITIVE SIDE EFFECT**
Increased ZEC transaction volume = stronger privacy set for all users (larger anonymity set benefits entire network)
### Success Metrics
**QUANTITATIVE METRICS**
Month 3 (Beta Launch):
- SDK published to npm/PyPI
- 10+ developers testing on testnet
- 100+ test transactions completed
- Zero critical security issues
Month 6 (Mainnet Launch):
- 30+ AI agents using WAP3
- 3+ AI frameworks integrated (MCP, LangChain, AutoGPT)
- 500+ ZEC transactions from autonomous agents
- 5+ developers building agent+ZEC applications
Month 12 (Post-Launch):
- 100+ AI agents on platform
- 10+ AI frameworks supported
- 2,000+ ZEC transactions
- 3+ enterprise AI deployments
- ZEC recognized as "privacy layer for AI payments"
**QUALITATIVE METRICS**
Developer Experience:
- "5-minute quickstart" achievable
- Positive feedback in GitHub issues
- Community contributions to SDK
Ecosystem Impact:
- Zcash Forum discussions mentioning AI agents
- Media coverage of ZEC + AI use case
- Other projects building on WAP3
Technical Quality:
- Clean security audit report
- No critical bugs in production
- High test coverage (>80%)
**MEASUREMENT METHODS**
Tracking:
- GitHub analytics (stars, forks, issues)
- NPM/PyPI download stats
- On-chain transaction counting
- Developer surveys (quarterly)
Reporting:
- Monthly updates on Zcash Forum
- Public dashboard showing metrics
- Final grant report with full data
### Startup Funding (USD)
$10,000
### Startup Funding Justification
This upfront funding covers essential costs required before milestone delivery can begin and is included within the total requested grant amount.
1. Infrastructure Setup ($2,000)
• Cloud accounts, development servers
• Testnet deployment infrastructure
• CI/CD pipeline configuration
2. Initial Development ($6,000)
• Month 1-1.5 compensation
• Allows full-time focus on architecture
• Critical foundation phase
3. Security Tools ($1,000)
• Audit tooling licenses
• Static analysis setup
• Penetration testing frameworks
4. Legal/Admin ($1,000)
• Initial legal review
• Contractor agreements
• Insurance/admin setup
This upfront allocation ensures uninterrupted development and reduces execution risk for later milestones.
Total Startup Funding: $10,000
### Milestone Details
```milestones.yaml
MILESTONE 1
Amount (USD): 8,000
Expected Completion Date: 2026-02-28
User Stories:
- "As an AI developer, I want to understand how WAP3 enables ZEC payments, so that I can evaluate it for my agent project"
- "As a Zcash community member, I want to see the technical architecture, so that I can provide feedback"
Deliverables:
- Architecture specification document (30+ pages)
- Privacy preservation proof-of-concept
- Zcash SDK integration prototype
- Technical blog post on approach
- GitHub repository initialized (public)
Acceptance Criteria:
ZCG committee reviews and provides feedback. POC demonstrates ZEC shielded payment from mock agent. Community feedback collected via forum post.
MILESTONE 2
Amount (USD): 10,000
Expected Completion Date: 2026-04-30
User Stories:
- "As an AI developer, I want a TypeScript SDK, so that I can integrate ZEC payments into my Node.js agent"
- "As a Python AI developer, I want a Python SDK, so that I can use ZEC in my LangChain/AutoGPT projects"
Deliverables:
- WAP3 TypeScript SDK v0.1 (npm package)
- WAP3 Python SDK v0.1 (PyPI package)
- Unit tests (>80% coverage)
- Basic documentation + quickstart
- 3+ code examples (MCP, LangChain, standalone)
Acceptance Criteria:
5+ external developers successfully test SDK on testnet. Zero critical bugs reported. Documentation enables "5-minute quickstart".
MILESTONE 3
Amount (USD): 8,000
Expected Completion Date: 2026-06-15
User Stories:
- "As an AI agent, I want escrow flows, so that I can safely pay for services with automatic release"
- "As a service provider, I want provable payment release, so that I trust delivering to agents"
Deliverables:
- HTLC-based escrow protocol implementation
- Zcash memo-based provenance system
- Escrow unit tests + integration tests
- Zcash testnet deployment
- Security review (third-party cryptographic audit)
Acceptance Criteria:
Escrow protocols pass external security review (no critical issues). 100+ testnet HTLC transactions executed successfully. Provenance logs correctly stored in Zcash memos and retrievable via viewing keys.
MILESTONE 4
Amount (USD): 8,000
Expected Completion Date: 2026-07-31
User Stories:
- "As an AI framework maintainer, I want WAP3 integration guides, so that I can add ZEC payment tools to my framework"
- "As a developer, I want to see real examples, so that I can build my own agent+ZEC application"
Deliverables:
- MCP tool integration (Anthropic standard)
- LangChain connector
- LlamaIndex tool plugin
- 5+ comprehensive tutorials
- Video walkthrough (15 min)
Acceptance Criteria:
All integrations working in respective frameworks. 10+ developers build demo agents using tutorials. Positive feedback from framework communities.
MILESTONE 5
Amount (USD): 6,000
Expected Completion Date: 2026-09-15
User Stories:
- "As a production user, I want mainnet-ready WAP3, so that I can deploy real AI agents with real ZEC payments"
- "As a security-conscious developer, I want confidence in safety, so that I trust using WAP3 in production"
Deliverables:
- Mainnet deployment (contracts + infrastructure)
- Production-grade documentation
- Security best practices guide
- Bug bounty program launched
- v1.0 release (SDK + contracts)
Acceptance Criteria:
3+ production AI agents using WAP3 on mainnet. 500+ mainnet ZEC transactions completed. No critical security incidents. 20+ GitHub stars on main repo.
Total Budget (USD): $50,000
• Startup Funding: $10,000
• Milestones (1–5): $40,000
• Total: $50,000
```
### Supporting Documents
```files.yaml
1. GitHub Repository: WAP3 Proof-of-Concept
URL: https://github.com/nelsonjingusc/wap3-agent-payment-poc
Description: Working implementation of agent payment escrow in an EVM-based prototype environment.
Demonstrates intent creation (AP2), payment triggers (X402), on-chain escrow, and automated settlement. 18/18 tests passing. This POC showcases the architecture patterns we will adapt for Zcash-native implementation.
2. Demo Video: WAP3 Agent Payment Flow
URL: https://youtu.be/G9UQgBpxtl4
Description: 1-minute walkthrough of autonomous agent payment lifecycle - intent creation, escrow deposit, task execution, proof submission, and automatic settlement. Shows real working code, not slides.
3. Technical Documentation
URL: https://github.com/nelsonjingusc/wap3-agent-payment-poc/blob/main/TECHNICAL.md
Description: Detailed smart contract API, architecture decisions, and integration patterns.
Note: Current POC is EVM-based for rapid prototyping. ZCG-funded work will adapt these patterns to Zcash-native HTLC escrow while maintaining privacy guarantees.
```