GlassBox AI 💎

Transparent Multi-Agent Systems with Trust Scoring

The first production-ready framework for building auditable, trustworthy multi-agent AI systems with runtime trust distribution.

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🎯 Vision

Transform AI from black boxes to glass boxes - where every decision is traceable, every agent is accountable, and trust is earned through verified outcomes.

Problem: Existing multi-agent frameworks (CrewAI, AutoGen) orchestrate agents but hide their reasoning. You see the final answer, not WHY it was chosen or WHO to trust.

Solution: GlassBox AI adds a transparent trust layer - agents debate, trust scores weight their votes, outcomes update reputations, and full provenance chains show exactly how decisions were made.

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📍 Current Status

Milestone 1 (In Progress): MCP Server MVP
Target: Feb 15, 2026
Status: 🟡 Building core components

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🗓️ Roadmap & Milestones

✅ Milestone 0: Research & Design (DONE)

• Analyze competitive landscape (LIME, SHAP, CrewAI, AutoGen, Vector Institute research)
• Identify unique positioning (trust scoring + claim verification + provenance)
• Define architecture (MCP server → orchestrator → trust DB)

🟡 Milestone 1: MCP Server MVP (Feb 13-15, 2026)

Goal: Working multi-agent MCP server that integrates with Windsurf

Deliverables:

• Project structure
• server.py - MCP protocol handlers
• orchestrator.py - Parallel GPT agent execution with 3 personas
• trust_db.py - SQLite persistence for trust scores
• requirements.txt and .env.example
• Dockerfile for GHCR deployment
• README with setup instructions
• Test with Windsurf locally

Agents (GPT-only for MVP):

• @architect - Long-term thinking, scalability focus
• @pragmatist - Ship fast, iterate, business value
• @critic - Edge cases, security, failure modes

Trust Mechanism:

• Initial scores: 0.85 for all agents
• Update formula: new_trust = old_trust + 0.1 * (outcome - old_trust)
• Weighted consensus by trust

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🔲 Milestone 2: Claim Verification Layer (Feb 16-20, 2026)

Goal: Add fact-checking to agent responses

Deliverables:

• verifier.py - Claim extraction from agent responses
• Source grounding validation (does citation support claim?)
• Confidence scoring per claim
• Provenance chain tracking (claim → agent → source → line number)

Example:

Agent says: "Use Redis for caching." [Source: docs.redis.io]
Verifier checks: Does source actually recommend Redis for this use case?
Result: ✅ Supported (confidence: 0.92)

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🔲 Milestone 3: Web Dashboard (Feb 21-28, 2026)

Goal: Visual interface for trust evolution and agent debates

Deliverables:

• FastAPI backend serving agent analysis API
• React frontend with:
  • Real-time agent conversation display
  • Trust score graphs over time
  • Provenance tree visualization
  • Manual trust adjustment controls
• Deployed demo at demo.glassbox-ai.dev

UI Mockup:

┌─────────────────────────────────────────┐
│ 🤖 Multi-Agent Analysis                │
├─────────────────────────────────────────┤
│ @architect (Trust: 0.92) 📈            │
│ "Use Postgres with materialized views" │
│                                         │
│ @pragmatist (Trust: 0.85) 📊           │
│ "Start with Redis, migrate later"      │
│                                         │
│ @critic (Trust: 0.88) ⚠️               │
│ "What's your eviction policy?"          │
├─────────────────────────────────────────┤
│ ⚖️ Weighted Consensus: Redis (0.87)    │
└─────────────────────────────────────────┘

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🔲 Milestone 4: Multi-Model Support (Mar 1-7, 2026)

Goal: Support Claude + GPT + Gemini for true agent diversity

Deliverables:

• Anthropic API integration
• Google Gemini API integration
• Agent pool with mixed models:
  • @architect → Claude Opus
  • @pragmatist → GPT-4o
  • @critic → Claude Sonnet
  • @innovator → Gemini Pro
• Cost tracking per agent/model

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🔲 Milestone 5: Production Hardening (Mar 8-15, 2026)

Goal: Enterprise-ready deployment

Deliverables:

• Rate limiting and retry logic
• Error recovery and fallbacks
• Observability (Prometheus metrics, OpenTelemetry traces)
• Security audit (API key handling, input validation)
• Load testing (100 concurrent analyses)
• Documentation site (docs.glassbox-ai.dev)

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🔲 Milestone 6: CLI & PyPI Release (Mar 16-22, 2026)

Goal: Shareable package anyone can install

Deliverables:

• glassbox CLI tool
• PyPI package: pip install glassbox-ai
• Usage examples and tutorials
• Blog post: "Building Transparent Multi-Agent Systems"
• LinkedIn case study with screenshots
• GitHub Sponsors / funding model

Usage:

pip install glassbox-ai

# Analyze a problem
glassbox analyze "Should we use Redis or Postgres?"

# View trust dashboard
glassbox trust-dashboard

# Update trust manually
glassbox update-trust architect --correct

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🏗️ Architecture

┌─────────────────────────────────────────────┐
│  Windsurf Chat / CLI / Web Dashboard       │
└─────────────────┬───────────────────────────┘
                  │ MCP Protocol / API
┌─────────────────▼───────────────────────────┐
│         MCP Server (server.py)              │
│  Tools: multi_agent_analyze,                │
│         get_trust_scores, update_trust      │
└─────────────────┬───────────────────────────┘
                  │
┌─────────────────▼───────────────────────────┐
│      Orchestrator (orchestrator.py)         │
│  - Parallel agent execution                 │
│  - Weighted consensus                       │
│  - Provenance tracking                      │
└──┬────────────┬────────────┬────────────────┘
   │            │            │
┌──▼──┐   ┌────▼───┐   ┌───▼────┐
│GPT-4│   │GPT-4o  │   │GPT-4   │
│Opus │   │        │   │Turbo   │
└─────┘   └────────┘   └────────┘
 @arch     @pragma      @critic

┌─────────────────────────────────────────────┐
│       Trust DB (trust_db.py)                │
│  SQLite: agent → trust score → history     │
└─────────────────────────────────────────────┘

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🎯 Success Metrics

Technical:

• <500ms latency for 3-agent analysis
• Trust score convergence within 10 iterations
• 95%+ uptime on demo deployment

Adoption:

• 100 GitHub stars by end of March
• 50 PyPI downloads/week
• 1 enterprise POC

Validation:

• Featured in a newsletter (e.g., TLDR AI, The Batch)
• 1 blog post or paper citing this work
• Positive feedback from 5 real users

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🚀 Quick Start (After Milestone 1)

Local Setup

git clone https://github.com/yourusername/glassbox-ai
cd glassbox-ai
pip install -r requirements.txt

# Add API key
cp .env.example .env
# Edit .env: OPENAI_API_KEY=sk-...

# Run MCP server
python server.py

Windsurf Integration

Add to ~/.codeium/windsurf/mcp_servers.json:

{
  "glassbox-ai": {
    "command": "python",
    "args": ["/path/to/glassbox-ai/server.py"]
  }
}

Restart Windsurf, then:

You: "Should we use Redis or Postgres for session storage?"

[Cascade invokes multi_agent_analyze]

🤖 3 agents analyzing...
✅ Consensus ready

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📂 Project Structure

glassbox-ai/
├── README.md              # This file
├── server.py              # MCP entry point
├── orchestrator.py        # Multi-agent logic
├── trust_db.py           # Trust persistence
├── verifier.py           # (Milestone 2) Claim checking
├── requirements.txt
├── Dockerfile
├── .env.example
├── .gitignore
├── tests/                # (Milestone 5) Test suite
├── docs/                 # (Milestone 5) Documentation
└── web/                  # (Milestone 3) Dashboard
    ├── backend/
    └── frontend/

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🤝 Contributing

We're in MVP phase. Contributions welcome after Milestone 1 is complete.

Roadmap priorities:

1. Core MCP server stability
2. Claim verification accuracy
3. Trust evolution algorithms
4. Multi-model integration

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📜 License

MIT (open source, shareable, production-ready)

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📧 Contact

Built by @yourname
LinkedIn: Your Profile

Building in public. Follow along for updates on transparent AI systems.

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🔗 Related Work

Inspiration:

• Vector Institute - Agentic Transparency
• TrustAgent Survey
• ADORE Paper (Atlassian)

Frameworks we build on:

• CrewAI - Multi-agent patterns
• AutoGen - Agent orchestration
• InterpretML - Glass box models

Our unique contribution: First to combine trust scoring + claim verification + provenance in a production multi-agent system.

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💎 Glass Box over ⬛ Black Box