Verification, not trust
AI claims are not proof. JUGNU is architected so that work is evidenced, verified, and attested before it earns reputation or settlement.
Evidence-based verification
AI can generate code.
JUGNU verifies what it actually does.
Evidence-based verification for AI-assisted software.
JUGNU provides bounded, evidence-driven assessment of AI-assisted software changes — connecting observations to reproducible evidence, verification, and actionable technical conclusions.
Need a bounded assessment of an AI-assisted software change? Explore JUGNU Services
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JUGNU AI-Assisted Code Safety Assessment
Assess → Verify → Report
A defined codebase or change is examined using relevant source, test, static-analysis, runtime, and configuration evidence. The result is a structured assessment built for technical decision-making.
- Findings
- Supporting evidence
- Verification status
- Confidence
- Limitations
- Recommendations
Proposed architecture
The conceptual JUGNU chain
The chain below describes JUGNU's proposed long-term architecture — not deployed protocol functionality. Today's commercial offering is the AI-Assisted Code Safety Assessment.
- Identity
- Authorization
- Action
- Evidence
- Verification
- Reputation
- Settlement
Security-first, devnet-first
Development begins on devnet with conservative status reporting. No mainnet readiness is claimed, and no protocol capability is presented as production functionality.
Built for the machine economy
As AI agents transact at machine speed, identity, authorization, and evidence must be verifiable infrastructure — not afterthoughts.
Community
Follow the development of JUGNU
Review the public repository, follow project updates, and stay connected as JUGNU evolves toward verifiable infrastructure for the AI-agent economy.