AgentLens

Website • Documentation • Integration Guide • Architecture • 中文文档

AgentLens is a trusted AI Agent discovery and launch platform. It helps users find task-specific Agents, understand their risk boundaries, and either use them in-platform, follow a guided integration path, or jump to the provider when an Agent must stay outside AgentLens.

By combining On-chain Audit Scores, Intel SGX TEE Attestation, Zero-Knowledge Proofs (ZK), and a Multi-Dimensional Dynamic Reputation Model (MDDRM), AgentLens ensures that Agent trust is verifiable, not just claimed.

🌐 Official Platform

Visit our live platform: AgentLens — Trusted AI Agent Selection

🚀 Features

• 📊 Dimensional Risk Profiling: Evaluates Agents across 6 dimensions (Security, Task Execution, Cognitive, Environment, Engineering, Compliance) to generate a comprehensive risk profile and scenario suitability recommendation.
• 🔐 Intel SGX TEE Attestation: All sandbox audits run inside hardware-isolated enclaves. Cryptographic proofs (MRENCLAVE) are anchored on-chain to guarantee execution integrity.
• 🛡️ Zero-Knowledge Proof Verification: Uses circom and snarkjs (Groth16/BN128) to prove audit score calculations and Agent identity fingerprints without exposing proprietary source code.
• ⚖️ Dynamic Reputation (MDDRM): On-chain reputation scores that dynamically adjust based on audit results, user reviews, appeal outcomes, and time decay.
• 🧭 Agent Discovery & Launch: Search, compare, and open task-specific Agents like lightweight apps — use supported Agents directly, follow setup guides, or hand off to external providers when trust or execution boundaries require it.
• 🏪 Trust-First Marketplace: A React-based frontend where buyers can browse, filter (by risk, TEE status, price, task type), and rent/purchase access to verified Agents.

🏗️ Architecture

graph TD
    subgraph "Developer"
        D[Developer Wallet] -->|stake + submit| R
    end

    subgraph "On-Chain (Polygon Edge)"
        R[AgentRegistry V3] -->|AuditRequested| L
        M[AgentMarketplace]
        Rev[ReviewRegistry]
        Z[ZkAuditVerifier]
    end

    subgraph "Off-Chain Infrastructure"
        L[Node.js Listener] -->|Trigger| S
        S[Docker Sandbox] <-->|QA and Execution| LLM[LLM Engine]
        S <-->|Execution| TEE[Intel SGX M6ce]
        S -->|Generate| ZKP[ZK Proof Generator]

        TEE -->|Attestation| L
        ZKP -->|Groth16 Proof| L
        L -->|recordAuditV2| R
    end

    subgraph "Users"
        B[Buyers] -->|Browse and Rent| M
        B -->|Leave Review| Rev
    end

⚡ Quick Start

Prerequisites

• Node.js 20+
• Docker & Docker Compose
• Rust (for compiling ZK circuits)
• Polygon Edge local node

Local Development

1. Install dependencies: bash cd contracts && npm install cd ../sandbox && npm install cd ../frontend && npm install

2. Start the local blockchain: bash cd infra/polygon-edge-local && docker compose up -d

3. Deploy smart contracts: bash cd contracts && npx hardhat run scripts/deployV3.js --network edge_local

4. Configure and start the frontend marketplace: ```bash cat > frontend/.env.local << EOF VITE_AUDIT_RPC_URL=http://localhost:18545 VITE_AUDIT_REGISTRY_ADDRESS= VITE_AUDIT_CHAIN_ID=302512 EOF

cd frontend && npm run dev ```

📊 Platform Walkthrough

The latest version of AgentLens has been fully redesigned — evolving from a pure on-chain Agent marketplace into a trusted AI Agent discovery, selection, and launch platform. The platform treats Agents like lightweight task apps: users can search for the Agent they need, compare structured facts, use supported Agents directly, or jump to the official/provider environment when an Agent has strict execution boundaries. The goal is to help users make evidence-based decisions, not rely on ads or star ratings.

1. Homepage — Trusted AI Agent Discovery

The homepage opens with a clean Hero section featuring a natural-language search bar and a "Browse All Agents" entry point. Below, Agents are categorized by real-world use cases (customer service automation, data analysis, dev assistant, workflow automation, etc.), and the 10 platform-maintained Agents with complete onboarding guides are highlighted.

Core design philosophy: No ads, no star ratings. Every Agent's scenario fit, risk level, integration method, onboarding difficulty, pricing, and official resources are structured fields — not marketing copy.

2. Agent Catalog — Multi-Dimensional Discovery

The Agent list page aggregates all 50+ Agents with search (by name / description / tag / scenario) and multi-dimensional filtering by risk level, onboarding difficulty, and guide availability. Each Agent card shows the seller's background, core scenario tags, risk level, onboarding difficulty, guide status, and an "Add to Compare" button.

Agents are organized by practical availability: Ready to Use (can be launched or rented through AgentLens), Guided Setup (complete onboarding guide available), and External Launch (trusted listing with handoff to the provider when the Agent runs outside AgentLens).

3. Agent Detail Page — Complete Decision Profile

Each Agent has a dedicated detail page providing a complete "selection decision profile" with the following modules:

4. Recommendation — Intelligent Selection Assistant

Not sure which Agent to choose? The recommendation page offers two matching modes:

• Free Rule Matching: Quickly filters candidate Agents based on structured conditions — task description, use case scenario, usage mode, preferred integration, and priority.
• Paid LLM Recommendation: Invokes a large language model for deep semantic understanding, delivering more precise recommendations with reasoning.

5. Agent Comparison — Side-by-Side Multi-Dimensional View

After adding multiple Agents to the comparison list, the compare page presents them side-by-side across basic info, capability dimensions, risk indicators, integration methods, and pricing — helping users make a final decision among candidates.

6. Publish Agent — Developer Onboarding Paths

The publish page provides developers with two clear listing paths:

• Submit Docker Image — Trusted Audit Path: For high-trust, high-risk Agents that want to appear in recommendation rankings. The platform pulls the image via manifest, audits network boundaries, behavioral evidence, and resource usage in a sandbox, and binds manifest hash + image digest to form the Agent's identity fingerprint.
• No Image Submission — Managed API/MCP Fast Track: For closed-source SaaS, early-stage validation, and externally hosted Agents. AgentLens performs access control, metering, health checks, and black-box testing via a gateway. Trust level will be lower than the audited image path.

🧪 Baseline Audit Report — Mainstream LLM Agent Benchmarks

To demonstrate that AgentLens differentiates real capability from marketing claims, we ran multiple AI Agents through the same audit pipeline (Docker start → health check → LLM dynamic Q&A → LLM judge → SGX TEE attestation → on-chain write-back) under identical scoring rules.

Class A — Tier-1 General LLM Agents

Observation: All three tier-1 Agents passed with perfect scores, satisfying LLM judge criteria and security boundary probing. Audit durations varied (GPT-4o ~6 min, Zhipu ~12 min), reflecting inference latency differences — but conclusions were identical, proving AgentLens judges purely on output quality, not vendor brand.

Class B — Agent-Native & Vertical Models

Class C — Failure Cases & Boundary Detection

Bottom line — verify before you hire. AgentLens replaces self-declared "trust me" claims with verifiable, hardware-anchored audit records that any wallet can inspect on-chain before paying.

🧩 Core Components

Smart Contracts (/contracts)

• AgentAuditRegistryV3: Implements the MDDRM reputation system, handling staking, audit results, appeals, and time-decay logic.
• AgentMarketplace: Manages Agent access rights, supporting daily rentals and permanent purchases with access control checks.
• ZkAuditVerifier: On-chain registry storing verified Groth16 proofs for audit scores and Agent fingerprints.

Audit Sandbox (/sandbox)

An isolated environment that automatically evaluates submitted Agents using an LLM engine. It generates 6-dimensional scores, performs security boundary analysis, and coordinates TEE attestation and ZK proof generation before writing results back to the blockchain.

Zero-Knowledge Circuits (/contracts/zk)

• AuditScoreVerifier: Proves that 6-dimensional scores and the overall weighted average are correctly computed from raw audit data.
• AgentFingerprint: Proves Agent identity and behavioral characteristics bound to a specific NFT Token ID without revealing the underlying code.

📖 Documentation

• Agent Integration Guide — How to build and submit your Agent for auditing.
• Verification Methods — Details on how AgentLens verifies Agent claims.
• TEE Production Status — Information about the SGX hardware enclave setup.

🛡️ Security & Trust

AgentLens takes security seriously. The entire architecture is designed to minimize trust assumptions: * Code Privacy: Developers don't need to expose source code; ZK proofs handle identity and characteristic verification. * Execution Integrity: TEE attestation ensures the audit sandbox has not been tampered with. * Economic Security: MDDRM slashing mechanisms economically penalize malicious or failing Agents.

Please see our SECURITY.md for vulnerability reporting guidelines.

🤝 About the Author & Meet Popo

Hi! I'm a student independently building AgentLens. My goal is to build a verifiable, trust-first infrastructure for the AI Agent economy.

Before entering the Web3 and AI space, I was a professional table tennis player. The discipline, precision, and quick reflexes required in competitive sports have deeply influenced my approa