Provenance Framework
Decentralized Storage for AI Agent Accountability

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"Building the infrastructure for trustworthy AI."

Solving the Problem of
Where to Store Provenance Data
The Challenge: Where do you store provenance data when AI agents need to prove what they did and why?
Our Answer: Swarm-based decentralized storage with MCP integration.
Why This Matters Now
Three Converging Trends
1. Regulatory Pressure is Accelerating
  • EU AI Act: Requires transparency and accountability for high-risk AI systems
  • GDPR: Data lineage and processing records mandatory
  • Industry Standards: Increasing demands for audit trails
  • Liability Concerns: "Who's responsible when AI makes mistakes?"
2. AI Agents are Proliferating
  • Autonomous agents making real decisions
  • Multi-agent systems with complex interactions
  • Need for accountability without human oversight
  • MCP (Model Context Protocol) enabling agent ecosystems
3. Decentralized Storage is Ready
  • Swarm Network: Production-ready decentralized storage
  • Mature Technology: Reliable, fast, cost-effective
  • Web3 Benefits: Without the typical trade-offs
  • Perfect Fit: For immutable, long-term metadata storage
Our Solution: Decentralized Provenance Storage
The Best of Both Worlds
What We Are Building
A decentralized provenance framework that combines:
Decentralized Storage
Swarm network for immutable, cost-effective storage
MCP Integration
For AI agent compatibility
Standard Formats
W3C PROV-O and custom schemas
Simple Tools
CLI, API, and agent-friendly interfaces
Core Principle
Separate storage from generation
  • Applications and agents generate provenance data
  • Our framework stores it independently
  • Anyone can verify it cryptographically
  • No vendor lock-in, no single point of failure
Key Innovation
Making decentralized storage practical for provenance
  • Web2 developer experience
  • Web3 trust and immutability
  • AI agent native integration
Key Benefits
For AI Developers
Native Agent Integration
  • MCP Server: AI agents can store/retrieve provenance automatically
  • Audit Trails: Complete record of agent decisions and data sources
  • Compliance Ready: Built-in support for AI Act requirements
  • Simple Integration: Works with existing agent frameworks
Developer Experience
  • CLI tools for quick integration
  • REST API for any language
  • Standard formats (JSON, PROV-O)
  • Cryptographic verification built-in
For Enterprises
Compliance & Governance
  • Independent Storage: Third-party verification for auditors
  • Immutable Records: Tamper-proof provenance data
  • Long-term Retention: Cost-effective for years of data
  • Regulatory Ready: GDPR, AI Act, industry standards
Business Value
  • No Vendor Lock-in: Open protocol, portable data
  • Predictable Costs: One-time storage payment, no monthly fees
  • Risk Reduction: Prove data lineage and decision processes
  • Competitive Advantage: Demonstrate trustworthy AI
Real-World Use Cases
AI Agent Decision Tracking
Scenario: Multi-agent system making automated decisions
Challenge: Need to audit which agent did what, with which data
Solution:
  • Each agent automatically logs provenance via MCP
  • Complete audit trail stored immutably on Swarm
  • Query by agent ID, timestamp, or decision type
  • Prove compliance with AI Act transparency requirements
Value: Regulatory compliance + system debugging + trust
Supply Chain Authenticity
Scenario: Product authenticity verification across supply chain
Challenge: Multiple parties need to trust product history
Solution:
  • Each party adds provenance data to Swarm
  • Cryptographic verification prevents fraud
  • No single party controls the records
  • Consumers can verify authenticity
Value: Brand protection + consumer trust + fraud prevention
Our Roadmap
Current: Foundation (Q4 2025 - Q1 2026)
SWIP-37 Specification
  • Complete technical specification published
  • W3C PROV-O integration defined
  • Metadata and TTL management specified
Reference Implementation
  • CLI toolkit for basic operations
  • Upload/download provenance data
  • Metadata querying and management
Next: AI Agent Integration (Q1-Q2 2026)
MCP Server
  • Model Context Protocol integration
  • AI agents can automatically store/retrieve provenance
  • Claude, GPT, and other agent compatibility
Attestation & Notarization
  • Blockchain timestamping for legal validity
  • Cryptographic attestation of data sources
  • Enhanced trust and verification
Future: Enterprise & Ecosystem (2026+)
Enterprise Features
  • Advanced access control and permissions
  • Organization-level management tools
  • Compliance reporting and dashboards
  • SLA and support options
Ecosystem Growth
  • Integrations with popular AI frameworks
  • Data warehouse and BI tool connectors
  • Industry-specific templates and standards
  • Partner network expansion
We're building iteratively based on user feedback
Get Involved
Pilot Partners
  • Priority access to new features
  • Direct influence on development
  • Co-marketing opportunities
Integration Partners
  • Technical collaboration
  • Joint go-to-market strategies
  • Ecosystem building
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