Enterprise-grade autonomous compliance monitoring, violation detection, and intelligent remediation system
Key Features • Architecture • Tech Stack • Getting Started • API Documentation • Contributing
- Overview
- Key Features
- System Architecture
- Tech Stack
- Project Structure
- Getting Started
- API Documentation
- Security
- Roadmap
- Contributing
- License
- Acknowledgments
AGEIS (Automated Governance & Enterprise Intelligence System) is a comprehensive enterprise compliance and governance platform designed to automate regulatory monitoring, detect violations, and provide intelligent remediation through advanced AI agents.
Built with modern cloud-native technologies, AGEIS provides real-time compliance monitoring across multiple regulatory frameworks including PCI-DSS, GDPR, DPDP, and custom organizational policies.
AGEIS transforms traditional compliance management by:
- 🔍 Continuous Monitoring - Real-time scanning of data sources for compliance violations
- 🤖 AI-Powered Detection - Intelligent pattern recognition using advanced ML models
- ⚡ Automated Remediation - Autonomous fixing of detected issues without human intervention
- 📊 Unified Dashboard - Centralized visibility into compliance posture across the enterprise
- 📝 Evidence Collection - Automated generation of compliance evidence and audit trails
- 🔐 Audit Readiness - Always-on audit trail with cryptographic signing
| Feature | Description |
|---|---|
| Multi-Framework Compliance | Support for PCI-DSS, GDPR, DPDP, SOC 2, ISO 27001, and custom frameworks |
| Real-Time Violation Detection | Continuous monitoring with instant alerting on policy breaches |
| AI-Powered Risk Assessment | ML-based scoring and prioritization of compliance risks |
| Automated Evidence Collection | Smart generation of compliance documentation and proofs |
| RAG-Enhanced Queries | AI-powered Q&A system trained on regulatory documents |
| WebSocket Real-Time Updates | Live streaming of compliance events and dashboard updates |
| Role-Based Access Control | Granular permissions with JWT-based authentication |
| API-First Design | RESTful APIs for seamless integration with existing systems |
- Regulatory Intelligence - Automated tracking of regulatory changes
- Compliance Scoring - Dynamic scoring based on control effectiveness
- Integration Hub - Connectors for AWS, Google Cloud, Azure, GitHub, GitLab, Slack
- Export Capabilities - PDF/CSV export of reports, evidence packages, and audit logs
- Explainable AI - Transparent AI decision-making with full audit trails
- Watcher Agents - Configurable data source monitors with pattern-based triggers
AGEIS implements a multi-agent architecture where specialized AI agents work collaboratively to maintain enterprise compliance:
graph TB
subgraph "AGEIS AI Agents"
W["Watcher Agent"] -->|"Monitors Data"| DB["Database"]
W -->|"Triggers Events"| EB["Event Bus"]
EB -->|"Processes Events"| I["Interpreter Agent"]
I -->|"Analyzes Context"| M["Monitor Agent"]
M -->|"Detects Violations"| R["Remediator Agent"]
R -->|"Takes Action"| DB
R -->|"Logs Actions"| AL["Audit Ledger"]
end
subgraph "External Systems"
API["API Gateway"] --> W
UI["Frontend UI"] --> API
end
| Agent | Responsibility | Key Functions |
|---|---|---|
| Watcher Agent | Data Source Monitoring | Polls databases, APIs, file systems; triggers events on changes |
| Interpreter Agent | Context Analysis | Understands regulatory context, maps violations to frameworks |
| Monitor Agent | Pattern Detection | Analyzes data streams for compliance violations using ML |
| Remediator Agent | Automated Fixes | Executes remediation actions, generates evidence, logs to audit |
- Watch - The Watcher Agent continuously monitors configured data sources
- Interpret - The Interpreter Agent analyzes changes in regulatory context
- Monitor - The Monitor Agent applies ML-based detection patterns
- Remediate - The Remediator Agent automatically fixes violations and generates evidence
Figure 1: AGEIS High-Level System Architecture showing the interaction between frontend, backend, AI agents, and external integrations.
flowchart TB
subgraph "Client Layer"
FE["Next.js Frontend<br/>Port 3001"]
Mobile["Mobile Clients"]
end
subgraph "API Gateway Layer"
CORS["CORS Middleware"]
Auth["JWT Authentication"]
Rate["Rate Limiting"]
end
subgraph "Application Layer"
API["FastAPI Backend<br/>Port 8000"]
subgraph "AI Agents"
Watcher["Watcher Agent"]
Interpreter["Interpreter Agent"]
Monitor["Monitor Agent"]
Remediator["Remediator Agent"]
end
subgraph "Services"
RAG["RAG Service"]
Gemini["Gemini AI Service"]
Compliance["Compliance Scoring"]
Evidence["Evidence Generator"]
end
subgraph "Detection Engine"
Patterns["Pattern Matching"]
Validators["Data Validators"]
Confidence["Confidence Scoring"]
end
end
subgraph "Data Layer"
PG["PostgreSQL<br/>Port 5432"]
RedisC["Redis<br/>Port 6379"]
Vector["Vector Store<br/>(RAG Index)"]
end
subgraph "Event Layer"
EB["Async Event Bus"]
end
FE --> CORS
CORS --> Auth
Auth --> Rate
Rate --> API
API --> Watcher
API --> Interpreter
API --> Monitor
API --> Remediator
Watcher --> EB
EB --> Interpreter
Interpreter --> Monitor
Monitor --> Remediator
API --> RAG
API --> Gemini
API --> Compliance
API --> Evidence
API --> Patterns
API --> Validators
API --> Confidence
Patterns --> PG
Validators --> PG
Confidence --> RedisC
RAG --> Vector
Remediator --> PG
Evidence --> PG
Figure 2: AGEIS Technology Stack showing the complete tooling and frameworks used.
| Technology | Version | Purpose |
|---|---|---|
|
3.11+ | Core runtime |
 |
0.109+ | Web framework |
 |
2.0+ | ORM |
 |
2.0+ | Data validation |
 |
3.9+ | JWT handling |
 |
0.29+ | PostgreSQL async driver |
|
7.x | Caching and pub/sub |
 |
Latest | AI/LLM integration |
 |
0.3+ | RAG implementation |
| Technology | Version | Purpose |
|---|---|---|
|
16.x | React framework |
 |
19.x | UI library |
|
5.x | Type safety |
 |
4.x | Styling |
 |
1.1.x | Component primitives |
 |
2.15+ | Charts |
 |
12.x | Animations |
 |
3.25+ | Schema validation |
| Technology | Purpose |
|---|---|
 |
Containerization |
 |
Orchestration |
 |
Primary database |
|
Cache and events |
AGEIS/
├── backend/ # FastAPI Backend
│ ├── main.py # Application entry point
│ ├── requirements.txt # Python dependencies
│ └── app/
│ ├── config.py # Configuration management
│ ├── database.py # Database connection and models
│ ├── deps.py # Dependency injection
│ ├── agents/ # AI Agents
│ │ ├── interpreter.py # Context interpretation
│ │ ├── monitor.py # Pattern detection
│ │ ├── remediator.py # Automated fixes
│ │ └── watcher.py # Data source monitoring
│ ├── api/ # API Routes
│ │ ├── auth.py # Authentication
│ │ ├── compliance.py # Compliance management
│ │ ├── dashboard_routes.py
│ │ ├── violations.py # Violation tracking
│ │ ├── watcher_routes.py # Watcher configuration
│ │ ├── ask_routes.py # RAG Q and A
│ │ ├── audit.py # Audit logs
│ │ ├── evidence.py # Evidence generation
│ │ ├── regulations.py # Regulation management
│ │ ├── integration.py # External integrations
│ │ ├── websocket.py # Real-time updates
│ │ └── health.py # Health checks
│ ├── detection/ # Detection Engine
│ │ ├── patterns.py # Pattern definitions
│ │ ├── validators.py # Data validation
│ │ └── confidence.py # Confidence scoring
│ ├── events/ # Event Bus
│ ├── models/ # SQLAlchemy Models
│ ├── rag/ # RAG Implementation
│ │ ├── chain.py # LangChain chain
│ │ ├── indexer.py # Vector indexing
│ │ └── retriever.py # Retrieval logic
│ ├── schemas/ # Pydantic Schemas
│ └── services/ # Business Services
│ ├── auth_service.py
│ ├── compliance_scoring.py
│ ├── evidence_generator.py
│ ├── gemini_service.py
│ └── redis_service.py
│
├── Frontend/ # Next.js Frontend
│ ├── app/
│ │ ├── dashboard/ # Dashboard page
│ │ ├── compliance/ # Compliance management
│ │ ├── violations/ # Violation tracking
│ │ ├── watcher/ # Watcher configuration
│ │ ├── ask/ # RAG Q and A interface
│ │ ├── explainability/ # AI explainability
│ │ └── export/ # Export utilities
│ ├── components/
│ │ ├── ui/ # Reusable UI components
│ │ ├── landing/ # Landing page components
│ │ └── layout-shell.tsx # Layout wrapper
│ ├── hooks/ # Custom React hooks
│ ├── lib/ # Utilities
│ ├── mockdata/ # Mock data for development
│ └── public/ # Static assets
│
├── docker/ # Docker configurations
│ ├── Dockerfile.backend
│ ├── Dockerfile.frontend
│ └── Dockerfile.gateway
│
├── docker-compose.yml # Docker Compose orchestration
├── .env.example # Environment variables template
└── README.md # This file
Ensure you have the following installed:
| Requirement | Minimum Version |
|---|---|
|
24.0+ |
|
3.8+ |
|
3.11+ |
 |
20.x (for local dev) |
 |
9.x |
git clone https://github.com/ageis-project/ageis.git
cd ageisCopy the example environment file and configure:
cp .env.example backend/.envEdit backend/.env with your settings:
# Required: Gemini API Key for AI features
GEMINI_API_KEY=your_gemini_api_key_here
# Application Settings
APP_NAME=AGEIS
DEBUG=false
HOST=0.0.0.0
PORT=8000
# Database (PostgreSQL)
DATABASE_URL=postgresql+asyncpg://postgres:postgres@postgres:5432/ageis
# Redis
REDIS_URL=redis://redis:6379
# JWT Secret (generate a secure key for production)
SECRET_KEY=your-super-secret-key-change-in-productionUsing Docker Compose:
# Build and start all services
docker-compose up --build
# Start in detached mode
docker-compose up -dAfter starting, access the services at:
| Service | URL | Description |
|---|---|---|
| Frontend | http://localhost:3001 | Next.js web application |
| Backend API | http://localhost:8000 | FastAPI REST API |
| API Docs | http://localhost:8000/docs | Swagger/OpenAPI documentation |
| API Redoc | http://localhost:8000/redoc | ReDoc API documentation |
Backend:
cd backend
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python -m uvicorn main:app --reload --port 8000Frontend:
cd Frontend
pnpm install
pnpm devAGEIS provides comprehensive REST APIs with automatic documentation.
| Category | Endpoints |
|---|---|
| Authentication | /api/auth/login, /api/auth/register, /api/auth/refresh |
| Dashboard | /api/compliance, /api/system_liveness, /api/compliance_trend |
| Compliance | /api/compliance/controls, /api/compliance/frameworks |
| Violations | /api/violations, /api/violations/{id}, /api/violations/{id}/remediate |
| Watchers | /api/watchers, /api/watchers/{id}/trigger |
| Regulations | /api/regulations, /api/regulations/{id} |
| Evidence | /api/evidence, /api/evidence/generate |
| Audit | /api/audit/logs, /api/audit/export |
| Ask AI | /api/ask, /api/ask/chat |
| Integrations | /api/integrations, /api/integrations/{provider} |
Visit the following URLs when the backend is running:
- Swagger UI: http://localhost:8000/docs
- ReDoc: http://localhost:8000/redoc
- OpenAPI JSON: http://localhost:8000/openapi.json
- ✅ JWT Authentication - Token-based authentication with refresh tokens
- ✅ Role-Based Access Control (RBAC) - Granular permissions
- ✅ Password Hashing - Secure password storage with bcrypt
- ✅ CORS Protection - Configurable cross-origin resource sharing
- ✅ Rate Limiting - API rate limiting to prevent abuse
- ✅ Input Validation - Pydantic schema validation
- ✅ SQL Injection Protection - Parameterized queries via SQLAlchemy
- ✅ Audit Logging - All actions logged with timestamps
- Change the default
SECRET_KEYin production - Use HTTPS in production environments
- Configure proper CORS origins for your domains
- Implement regular security audits
- Keep dependencies updated
- Multi-Tenancy Support - Isolated environments for multiple organizations
- Advanced ML Models - Custom-trained models for specific industries
- Blockchain Audit Trail - Immutable audit logging
- Mobile Applications - iOS and Android apps
- More Integration Connectors - ServiceNow, Jira, Splunk, etc.
- Custom Policy Engine - No-code policy definition
- Compliance Forecasting - Predictive compliance analytics
- Auto-Scaling - Kubernetes deployment manifests
We welcome contributions! Please see our Contributing Guide for details.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Python: Follow PEP 8, use Black formatter
- TypeScript: Follow ESLint rules, use Prettier
- Commits: Use Conventional Commits format
This project is licensed under the MIT License - see the LICENSE file for details.
MIT License
Copyright (c) 2025 AGEIS (Automated Governance & Enterprise Intelligence System)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
- FastAPI - For the amazing web framework
- Next.js - For the powerful React framework
- Google Gemini - For AI capabilities
- LangChain - For RAG implementation
- Radix UI - For accessible component primitives
- All Contributors - For their valuable contributions
Built with ❤️ by Team CodeFirstThinkLater
