🧠 BAE Academic System - Scenario 1 Implementation

Business Autonomous Entities: Adaptive Academic System

This project implements the Scenario 1: Initial System Generation phase of the BAE (Business Autonomous Entities) proof of concept, as specified in the doctoral thesis "Agentes Baseados em LLM como Entidades Autônomas de Negócio: Uma Nova Arquitetura para Construção Adaptativa de Sistemas de Informação".

🎯 Project Status: Scenario 1 Core Components ✅

✅ Completed Components

1. Student BAE (Business Autonomous Entity)

• ✅ Domain entity representative for "Student" entity
• ✅ Natural language business request interpretation
• ✅ Domain knowledge preservation and semantic coherence
• ✅ SWEA coordination plan generation
• ✅ Business vocabulary management
• ✅ Context adaptation for different organizational settings

2. OpenAI GPT-4o-mini Integration

• ✅ Domain-focused LLM client wrapper
• ✅ Semantic coherence validation capabilities
• ✅ Business request interpretation methods
• ✅ Code generation with domain entity focus

3. Context Store

• ✅ Domain knowledge persistence
• ✅ Business vocabulary preservation
• ✅ Agent memory management
• ✅ Evolution history tracking (for Scenario 2)

4. Base Agent Framework

• ✅ Common agent functionality (BAE + SWEA)
• ✅ Memory management and interaction logging
• ✅ Error handling and performance metrics
• ✅ Task validation and response formatting

5. Prompt Templates

• ✅ Student schema generation (domain entity focus)
• ✅ Backend generation (SWEA coordination)
• ✅ Frontend generation (business vocabulary)

6. Test Suite

• ✅ All core components validated
• ✅ Scenario 1 workflow simulation
• ✅ 5/5 tests passing

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🔬 Scenario 1: Initial System Generation

Objective: Demonstrate automatic creation of functional system from natural language through domain entity autonomy.

Input: HBE request: "Create a system to manage students with name, registration number, and course"

Student BAE Process:

1. 🧠 Interpret business request using domain knowledge
2. 📋 Extract domain attributes and business vocabulary
3. 🎯 Create SWEA coordination plan maintaining semantic coherence
4. 📚 Preserve domain knowledge for reusability
5. ✅ Validate coordination plan completeness

Expected SWEA Coordination:

• Step 1: StudentBAE generates domain entity schema
• Step 2: BackendSWEA creates FastAPI backend
• Step 3: DatabaseSWEA creates persistence layer
• Step 4: FrontendSWEA generates Streamlit UI

Success Criteria:

• ⏱️ Generation time < 3 minutes
• ✅ 100% functional system
• 🎯 Domain entity autonomy maintained
• 📚 Semantic coherence preserved

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🏗️ Architecture

HBE (Human Business Expert)
    ↓ (natural language with business vocabulary)
Student BAE (Domain Entity Representative)
    ↓ (domain interpretation & SWEA coordination)
Context Store (Domain Knowledge Preservation)
    ↓ (coordination plan)
SWEA Agents (BackendSWEA, FrontendSWEA, DatabaseSWEA, TestSWEA)
    ↓ (generated artifacts with semantic coherence)
Functional System (API + UI + Database)

🛠️ Technology Stack

• Python 3.11+ - Core implementation language
• OpenAI GPT-4o-mini - Domain entity reasoning and code generation
• FastAPI - Backend framework (to be generated by SWEA)
• Streamlit - Frontend framework (to be generated by SWEA)
• SQLite - Database (to be generated by SWEA)
• Pydantic - Domain entity validation
• LangGraph - Agent orchestration (planned)

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🚀 Quick Start

1. Setup Environment

cd bae_academic_system
pip install -r requirements.txt

2. Configure OpenAI API Key

Create a .env file or update config.py:

OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4o-mini

3. Run Core Tests

python test_scenario1.py

Expected output: 5/5 tests passed ✅

4. Test Student BAE Independently

from agents.student_bae import StudentBAE

# Initialize Student BAE
student_bae = StudentBAE()

# Test business request interpretation
result = student_bae.handle_task("interpret_business_request", {
    "request": "Create a system to manage students with name, registration number, and course",
    "context": "academic"
})

print(result)

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📋 Next Implementation Steps

Phase 2: Complete SWEA Agents (Week 2)

1. BackendSWEA Implementation

• FastAPI code generation with domain focus
• SQLAlchemy model generation
• Database migration scripts
• API endpoint generation with business vocabulary

2. FrontendSWEA Implementation

• Streamlit UI generation with business terminology
• Form creation for domain entity operations
• Business-friendly error handling
• Real-time data refresh capabilities

3. DatabaseSWEA Implementation

• Database schema generation
• Business rule preservation in constraints
• Domain integrity validation
• Migration script generation

4. Runtime Kernel Implementation

• BAE-SWEA orchestration
• Dynamic file generation and loading
• Real-time system assembly
• Performance monitoring

Phase 3: Complete Scenario 1 Execution (Week 3)

1. End-to-End Integration

• Complete workflow execution
• File generation and deployment
• System startup automation
• Performance validation

2. Scenario 1 Validation

• <3 minute generation time
• 100% functional system
• Semantic coherence validation
• Business vocabulary preservation

3. Preparation for Scenario 2

• Runtime evolution capabilities
• Domain knowledge preservation
• Migration script generation
• Zero-downtime updates

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🧪 Testing Framework

Current Test Coverage

✅ OpenAI Client - LLM integration ready
✅ Context Store - Domain knowledge preservation
✅ Base Agent - Agent framework functional
✅ Student BAE - Domain entity representative working
✅ Scenario 1 Workflow - Coordination plan validated

Test Scenarios

1. Unit Tests - Individual component validation
2. Integration Tests - Agent interaction validation
3. Domain Tests - Business vocabulary preservation
4. Performance Tests - Generation time validation
5. End-to-End Tests - Complete workflow validation

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📊 Success Metrics (Scenario 1)

Quantitative Metrics

• ⏱️ Response Time: < 3 minutes (target)
• ✅ Success Rate: 100% functional system
• 🎯 Domain Coherence: Semantic alignment validation
• 📚 Vocabulary Preservation: Business terminology maintained

Qualitative Metrics

• 🧠 Domain Entity Autonomy: Student BAE operates independently
• 🔗 Semantic Coherence: Business concepts aligned with technical artifacts
• 🎭 HBE Usability: Natural language interaction successful
• 🔄 SWEA Coordination: Effective agent collaboration

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📚 Documentation References

• docs/PROOF_OF_CONCEPT.md - Complete scenario specifications
• docs/BAE_IMPLEMENTATION_GUIDE.md - Technical implementation details
• docs/IMPLEMENTATION_CHECKLIST.md - 3-week execution plan
• docs/PROMPT_TEMPLATES.md - LLM prompt specifications

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🤝 Domain Entity Focus

This implementation emphasizes the innovative BAE approach where:

1. Student BAE represents the domain entity as a living, autonomous agent
2. Business vocabulary is preserved throughout all technical artifacts
3. Semantic coherence is maintained between domain concepts and code
4. Domain knowledge is preserved for cross-organizational reusability
5. SWEA agents are coordinated by BAEs, not operating independently

This differs from traditional LMA approaches by focusing on domain entity autonomy rather than software engineering role simulation.

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🎉 Achievement Summary

✅ Scenario 1 Core Components Successfully Implemented

• 🧠 Student BAE functioning as domain entity representative
• 🔗 OpenAI GPT-4o-mini integration ready for domain reasoning
• 📚 Domain knowledge preservation and semantic coherence capabilities
• 🎯 SWEA coordination planning with business vocabulary focus
• ⚡ All tests passing (5/5) with sub-second performance

Ready for next phase: SWEA agent implementation and complete Scenario 1 execution.

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Project Status: 🟢 Phase 1 Complete - Ready for Phase 2 SWEA Implementation