AGEIS

Automated Governance & Enterprise Intelligence System

Enterprise-grade autonomous compliance monitoring, violation detection, and intelligent remediation system

Key Features • Architecture • Tech Stack • Getting Started • API Documentation • Contributing

Overview

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.

What We Do

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

Key Features

Core Features

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

Additional Features

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

🎯 Spotlight Feature: AI-Powered Autonomous Compliance Agents

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 Capabilities

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

How It Works

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

System Architecture

High-Level Architecture Diagram

Figure 1: AGEIS High-Level System Architecture showing the interaction between frontend, backend, AI agents, and external integrations.

Component Architecture

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

Tech Stack

Technology Overview

Figure 2: AGEIS Technology Stack showing the complete tooling and frameworks used.

Backend Technologies

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

Frontend Technologies

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

Infrastructure and DevOps

Technology	Purpose
	Containerization
	Orchestration
	Primary database
	Cache and events

Project Structure

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

Getting Started

Prerequisites

Ensure you have the following installed:

Requirement	Minimum Version
	24.0+
	3.8+
	3.11+
	20.x (for local dev)
	9.x

Installation

1. Clone the Repository

git clone https://github.com/ageis-project/ageis.git
cd ageis

2. Configure Environment Variables

Copy the example environment file and configure:

cp .env.example backend/.env

Edit 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-production

3. Build and Start Services

Using Docker Compose:

# Build and start all services
docker-compose up --build

# Start in detached mode
docker-compose up -d

Running the Application

After 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

Local Development (Without Docker)

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 8000

Frontend:

cd Frontend
pnpm install
pnpm dev

API Documentation

AGEIS provides comprehensive REST APIs with automatic documentation.

API Endpoints Overview

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}`

Interactive API Docs

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

Security

Security Features

✅ 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

Security Best Practices

Change the default SECRET_KEY in production
Use HTTPS in production environments
Configure proper CORS origins for your domains
Implement regular security audits
Keep dependencies updated

Roadmap

Upcoming Features

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

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

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

Code Style

Python: Follow PEP 8, use Black formatter
TypeScript: Follow ESLint rules, use Prettier
Commits: Use Conventional Commits format

License

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.

Acknowledgments

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

Name		Name	Last commit message	Last commit date
Latest commit History 40 Commits
Frontend		Frontend
backend		backend
docker		docker
output		output
.dockerignore		.dockerignore
.gitignore		.gitignore
README.md		README.md
docker-compose.yml		docker-compose.yml

Folders and files

Latest commit

History

Repository files navigation