Automated AI Assessment (AAA) — Intelligent Agentic AI Feasibility Platform¶

TL;DR: Enterprise-grade system that evaluates automation requirements for autonomous AI implementation, providing comprehensive feasibility assessments, pattern matching, and implementation guidance with 95-98% autonomy levels.

Stack: Python 3.10+ • FastAPI • Streamlit • Redis • Docker • OpenAI/Claude/Bedrock • FAISS • SQLAlchemy
Repo: GitHub ↗

🪄 Demo¶

Real-time agentic AI assessment with multi-provider LLM support, pattern matching, and automated diagram generation

✨ Features¶

🤖 Autonomous Agent Evaluation - Multi-dimensional scoring with 90%+ accuracy across reasoning complexity, decision boundaries, and workflow automation
🧠 Multi-Provider LLM Intelligence - Seamless integration with OpenAI (GPT-4, GPT-5, o1), Anthropic Claude, AWS Bedrock, and custom HTTP endpoints
🎯 Specialized Pattern Library - 5 APAT patterns with 95-98% autonomy levels, plus traditional automation patterns with FAISS vector similarity search
📊 AI-Generated Architecture - Context, Container, Sequence, C4, Infrastructure, and Tech Stack diagrams with Draw.io export capabilities
🛡️ Enterprise Security - Advanced prompt defense with 8 specialized detectors, multi-language attack detection, and comprehensive audit trails
🚀 Production-Ready Infrastructure - Docker deployment, Redis caching, session management, and real-time monitoring with 90% test coverage

🧠 Architecture¶

flowchart TB
    subgraph "Frontend Layer"
        UI[Streamlit UI]
        API[FastAPI Backend]
    end

    subgraph "AI Processing Layer"
        LLM[Multi-Provider LLM]
        FAISS[Vector Search]
        PATTERNS[Pattern Library]
    end

    subgraph "Security Layer"
        DEFENSE[Prompt Defense]
        AUDIT[Audit System]
        VALIDATE[Input Validation]
    end

    subgraph "Data Layer"
        REDIS[Redis Cache]
        DISK[Disk Storage]
        EXPORT[Export Engine]
    end

    UI --> API
    API --> LLM
    API --> FAISS
    FAISS --> PATTERNS
    API --> DEFENSE
    DEFENSE --> AUDIT
    API --> VALIDATE
    API --> REDIS
    API --> DISK
    API --> EXPORT

    LLM -.-> OPENAI[OpenAI GPT-4/5]
    LLM -.-> CLAUDE[Anthropic Claude]
    LLM -.-> BEDROCK[AWS Bedrock]

🎯 What Makes This Special¶

This system represents a paradigm shift from traditional automation assessment to autonomous agentic AI evaluation. Unlike conventional tools that focus on rule-based automation, AAA uses advanced AI reasoning to assess requirements for multi-agent systems with 95-98% autonomy levels.

The platform combines cutting-edge LLM technology with enterprise-grade security and production infrastructure. It features a sophisticated pattern matching system using FAISS vector search, comprehensive prompt defense mechanisms, and real-time diagram generation capabilities. The system can evaluate complex business requirements and recommend appropriate agentic architectures, from single autonomous agents to hierarchical multi-agent systems.

What sets this apart is the intelligent tech stack generation - the system doesn't just match patterns, it uses LLM-driven analysis to generate contextual technology recommendations based on specific requirements, domain constraints, and successful implementation patterns.

🚀 Technical Highlights¶

Core Architecture¶

FastAPI Backend: Async REST API with automatic OpenAPI documentation and security middleware
Streamlit Frontend: Interactive web interface with real-time updates and diagram visualization
Multi-Provider LLM: Unified interface supporting OpenAI, Anthropic, AWS Bedrock, and custom endpoints
FAISS Vector Search: High-performance similarity matching for pattern library with 384-dimensional embeddings

Production Infrastructure¶

Docker Deployment: Multi-stage containerization with production and development configurations
Redis Caching: Session state management and performance optimization with LRU eviction
Monitoring: Comprehensive health checks, performance metrics, and audit trail logging
Security: 8-layer prompt defense system with multi-language attack detection and enterprise constraints

Developer Experience¶

Code Quality: Black formatting, Ruff linting, MyPy type checking with 90%+ coverage
Testing: Pytest with async support, hypothesis property-based testing, 90% minimum coverage
Documentation: Auto-generated API docs, comprehensive guides, and architecture documentation
CI/CD: Make-based build system with quality gates and automated testing

📊 Key Metrics¶

Test Coverage: 90%+ across unit, integration, and end-to-end tests
Response Time: <2s for pattern matching, <5s for LLM-generated recommendations
Security: Zero known vulnerabilities with comprehensive prompt injection defense
Scalability: Handles 1000+ concurrent sessions with Redis clustering support
Accuracy: 90%+ accuracy in agentic suitability assessment across diverse domains

🛠️ Development Process¶

Built using modern Python development practices with comprehensive tooling ecosystem. Features async-first architecture throughout the stack, dependency injection via service registry, and interface-based design with protocols. Implements comprehensive error handling with custom exception hierarchy and security-aware logging that prevents sensitive data exposure.

🎨 User Experience¶

The interface provides an intuitive workflow from requirement submission through AI-powered Q&A to comprehensive feasibility assessment. Features real-time progress tracking, interactive diagram generation with full-screen viewing, and multi-format export capabilities (JSON, Markdown, HTML). The system automatically opens in the browser and provides contextual guidance throughout the assessment process.

This project demonstrates expertise in enterprise AI system architecture, multi-provider LLM integration, production-ready security implementation, and modern Python development practices. It showcases the ability to build sophisticated AI-powered applications that solve real business problems while maintaining enterprise-grade security and performance standards.