🛡️ UAAL - Universal Agent Action Layer

The Zero-Trust Control Plane for AI Agents

Deploy autonomous AI agents safely with enterprise-grade governance, compliance, and security controls.

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🎯 What is UAAL?

UAAL is the missing governance layer for AI agents. It sits between your AI agents and the systems they interact with, enforcing:

✅ Zero-Trust Access - Least-privilege policies for every agent action
✅ Human-in-the-Loop - Approval workflows for high-risk operations
✅ Spend Controls - Set and enforce budget limits per agent/team
✅ Complete Audit Trail - Every action logged for compliance
✅ Action Replay - Simulate what-if scenarios before execution
✅ Instant Rollback - Undo any agent action with one click
✅ Multi-Provider - Works with OpenAI, Anthropic, Google, any LLM

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🔥 Why UAAL?

The Problem

AI agents can now act autonomously across your systems - accessing databases, calling APIs, moving money, and making decisions. But:

• 🚨 80% of companies have experienced unintended AI agent actions
• 💸 No visibility into AI spending until the bill arrives
• ⚖️ Compliance teams can't audit what agents did
• 🔓 Traditional IAM doesn't cover AI agents

The Solution

UAAL creates a control plane that standardizes, governs, and secures all AI agent actions - giving you:

• Visibility: See every action in real-time
• Control: Enforce policies before actions execute
• Compliance: Complete audit logs for SOC2, HIPAA, GDPR
• Safety: Rollback dangerous actions instantly

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🏗️ Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   OpenAI    │     │  Anthropic  │     │   Gemini    │
│   Agents    │────▶│   Agents    │────▶│   Agents    │
└─────────────┘     └─────────────┘     └─────────────┘
       │                   │                    │
       └───────────────────┼────────────────────┘
                           ▼
                   ┌───────────────┐
                   │  UAAL Control │
                   │     Plane     │
                   └───────────────┘
                           │
            ┌──────────────┼──────────────┐
            ▼              ▼              ▼
      [Policy       [Approval      [Audit
       Engine]       Workflow]      Logger]
            │              │              │
            └──────────────┼──────────────┘
                           ▼
                   ┌───────────────┐
                   │  Target APIs  │
                   │   & Systems   │
                   └───────────────┘

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🚀 Quick Start

Installation

# Clone the repo
git clone https://github.com/LOLA0786/UAAL.git
cd UAAL

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the server
uvicorn app_v2:app --reload --port 8000

Send Your First Action

from agent_sdk import send_to_uaal

# Agent wants to send an email
result = send_to_uaal(
    adapter="openai_function",
    agent_output={
        "action": "send_email",
        "to": "ceo@company.com",
        "subject": "Q4 Results"
    },
    require_approval=True  # High-risk action
)

print(f"Action ID: {result['action_id']}")
print(f"Status: {result['status']}")  # "pending_approval"

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📚 Core Features

1. Universal Action Schema

All agent actions are normalized to a standard format:

{
  "action_id": "act_abc123",
  "agent_id": "gpt-4-analyst",
  "action_type": "api_call",
  "target": "stripe.com/v1/charges",
  "parameters": {...},
  "risk_level": "high",
  "estimated_cost": 150.00
}

2. Policy Engine

Define rules that automatically approve, block, or require human review:

# Block actions over $10k
policy = {
    "rule": "block_if",
    "condition": "estimated_cost > 10000",
    "action": "require_approval"
}

3. Approval Workflows

Route high-risk actions to the right humans:

# Approve via API
POST /api/v1/actions/{action_id}/approve
{
    "approver": "cfo@company.com",
    "notes": "Approved for Q4 budget"
}

4. Spend Tracking

Monitor and limit AI spending in real-time:

# Set budget limits
POST /api/v1/agents/{agent_id}/budget
{
    "daily_limit": 500,
    "monthly_limit": 10000,
    "alert_threshold": 0.8
}

5. Action Replay & Simulation

Test what-if scenarios before executing:

# Simulate without executing
result = replay_action(
    action_id="act_abc123",
    simulation_mode=True
)

6. Instant Rollback

Undo any action with full audit trail:

# Rollback a payment
POST /api/v1/actions/{action_id}/undo
{
    "reason": "Incorrect amount",
    "undone_by": "ops@company.com"
}

---

🎯 Use Cases

FinTech

• Problem: AI agent moved $50K to wrong account
• Solution: UAAL requires approval for transfers >$1K, maintains audit trail for compliance

Healthcare

• Problem: Need HIPAA audit logs for AI accessing patient data
• Solution: UAAL logs every data access with timestamps, user IDs, and purposes

E-Commerce

• Problem: AI agent gave 90% discount to all customers
• Solution: UAAL policy engine blocks discounts >50%, rollback fixed the mistake

SaaS Platforms

• Problem: No visibility into which AI agent is burning through API credits
• Solution: UAAL tracks costs per agent, alerts when budgets are exceeded

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UAAL — Verifiable AI Decision Infrastructure

UAAL (Universal AI Authorization Layer) ensures that autonomous AI actions are provable, auditable, and tamper-evident.

This is not logging. This is cryptographic decision accountability.

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Why UAAL Exists

Modern AI agents can:

• change prices
• approve transactions
• trigger emails
• modify systems

Traditional logs can be edited. UAAL produces immutable decision evidence that can be verified independently.

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What UAAL Guarantees

For every AI action:

1. Authorization
   • Every action is checked against an explicit policy
2. Evidence
   • A structured decision record is generated
3. Immutability
   • Evidence is cryptographically hashed
4. Independent Verification
   • Anyone can verify integrity without trusting UAAL

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Independent Verification (Auditors / Regulators)

Verification does not require:

• UAAL runtime
• Source code access
• Secrets
• Database access

Install verifier

pip install uaal-verify


Verify all AI decisions for a day
uaal-verify day YYYY-MM-DD

Verify a single AI decision
uaal-verify record <decision_id>

Verify a date range
uaal-verify range YYYY-MM-DD YYYY-MM-DD


If any evidence was modified, verification fails deterministically.

Regulatory Mapping
EU AI Act (High-Risk Systems)

Article 12: Logging → UAAL evidence

Article 14: Human oversight → uaal-approvals

Article 15: Accuracy & robustness → tamper detection

SOC 2

CC7.2: Change detection → Merkle verification

CC5.3: Control enforcement → policy gating

CC8.1: Auditability → independent verifier

RBI / Financial Regulators

Non-repudiation of automated decisions

Post-facto audit of AI actions

Separation of execution and verification

What UAAL Is NOT

❌ Not an LLM wrapper

❌ Not a monitoring dashboard

❌ Not prompt logging

UAAL is compliance infrastructure for autonomous systems.

Summary (for auditors)

“UAAL allows any third party to independently verify that AI decisions
were authorized, unmodified, and policy-compliant.”

That is the core guarantee.


This doc alone is enough for:
- enterprise security review
- compliance conversations
- regulator discussions

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# 3️⃣ Integration Example (distribution lever)

We’ll do **CrewAI-style** because it’s simple and familiar.

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## 📦 Example: CrewAI + UAAL (20 lines)

**Where**
- New repo or folder: `uaal-crewai-demo/`
- Or add to `uaal-regulated-ai-demo/`

---

### `crew_agent.py`

```python
from uaal import authorize
from uaal.approvals import require_approval

def pricing_agent(new_price: int):
    decision = authorize(
        agent="pricing-agent",
        action="update_price",
        payload={"new_price": new_price}
    )

    if not decision.allowed:
        raise Exception("Blocked by policy")

    if new_price > 500:
        require_approval(decision)

    return "price updated"

What this demonstrates (important)

CrewAI / agents stay unchanged

UAAL is a drop-in gate

Policies are enforced before action

Evidence is generated automatically

Verification is external

This is exactly what enterprises want:

“Don’t rewrite my agents. Just control them.”





## 🔌 Integrations

### Supported AI Providers
- ✅ OpenAI (GPT-4, GPT-3.5)
- ✅ Anthropic (Claude 3.5 Sonnet, Claude 3 Opus)
- ✅ Google (Gemini Pro, Gemini Ultra)
- ✅ Open-source models via API
- 🔜 AWS Bedrock
- 🔜 Azure OpenAI

### Notification Channels
- ✅ Webhooks
- 🔜 Slack
- 🔜 Microsoft Teams
- 🔜 Email
- 🔜 PagerDuty

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## 📊 Dashboard

UAAL includes a web dashboard for monitoring and management:
```bash
# Access at http://localhost:8000/dashboard
open http://localhost:8000/dashboard

Dashboard Features:

• Real-time action feed
• Approval queue
• Spend analytics
• Anomaly alerts
• Audit log viewer

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🔒 Security & Compliance

Zero-Trust Architecture

• Every action authenticated and authorized
• Least-privilege access by default
• No ambient authority

Audit & Compliance

• SOC 2 Type II ready (audit logs)
• HIPAA compliant data handling
• GDPR data retention policies
• Complete action replay for investigations

Enterprise Features

• 🔜 SSO/SAML integration
• 🔜 Role-Based Access Control (RBAC)
• 🔜 Multi-tenancy
• 🔜 99.9% SLA with monitoring

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🗺️ Roadmap

Q1 2025

• ✅ Core action standardization
• ✅ Approval workflows
• ✅ Basic policy engine
• 🚧 Spend tracking dashboard
• 🚧 Replay/simulation engine

Q2 2025

• SSO/SAML integration
• Advanced anomaly detection
• Slack/Teams approvals
• Multi-tenancy support

Q3 2025

• SOC 2 Type II certification
• GraphQL API
• Action marketplace
• Advanced analytics

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Development Setup

# Install dev dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/

# Run linter
black . && flake8 . && mypy .

# Run locally
uvicorn app_v2:app --reload

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📄 License

MIT License - see LICENSE for details

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🆚 UAAL vs. Competitors

Feature	UAAL	Astrix ACP	Microsoft Agent 365	LangChain
Zero-Trust	✅	✅	✅	❌
Multi-Provider	✅	❌	❌	✅
Spend Controls	✅	❌	❌	❌
Action Replay	✅	❌	❌	❌
Open Source	✅	❌	❌	✅
Approval Flows	✅	Limited	✅	❌

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💬 Community & Support

• Docs: docs.uaal.dev (coming soon)
• Discord: Join our community (coming soon)
• Email: support@uaal.dev
• Issues: GitHub Issues

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📈 Status

⚠️ Alpha Release - UAAL is under active development. Not recommended for production use yet.

• Current Version: 0.2.0
• Production Ready: Q2 2025 (estimated)
• Contributors: 1
• Stars: ⭐ Give us a star if this interests you!

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🙏 Acknowledgments

Built with inspiration from:

• Zero-Trust security principles
• Policy-as-Code movement
• Enterprise IAM patterns
• AI safety research

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Made with 🛡️ by the UAAL Team

Website • Twitter • LinkedIn

CHANDAN GALANI, AI ENTHUSIAST +91-9326176427