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Development Overview

Introduction​

This document provides a high-level overview of the Engineering AI Agent development architecture, principles, and key components. It serves as an entry point for developers to understand the system before diving into specific sections.

System Architecture​

The Engineering AI Agent follows a microservices architecture pattern, with clear separation of concerns across multiple specialized services. The system is designed to be scalable, maintainable, and resilient.

Key Design Principles​

  1. Modularity: Components are designed with clear interfaces and separation of concerns
  2. Scalability: Services can scale independently based on demand
  3. Resilience: System continues functioning despite partial failures
  4. Observability: Comprehensive logging, monitoring, and tracing
  5. Security: Zero-trust architecture with auth at all service boundaries
  6. AI-first: AI capabilities are integrated throughout the system

Technology Stack​

  • Backend Services: Python microservices with FastAPI
  • AI/ML: OpenAI, Hugging Face, LangChain
  • Data Storage: PostgreSQL, Redis, Vector Databases
  • Infrastructure: Kubernetes, Docker
  • CI/CD: Jenkins, GitHub Actions
  • Monitoring: Prometheus, Grafana
  • Messaging: Kafka, RabbitMQ

Development Workflow​

The development process follows these key steps:

  1. Requirements Analysis: Understanding and capturing user needs
  2. Design: Creating detailed specifications for components
  3. Implementation: Developing and testing features
  4. Review: Code review and quality assurance
  5. Deployment: Staged rollout across environments
  6. Monitoring: Continuous observation of system behavior

Documentation Structure​

The development documentation is organized into the following key sections:

  • Services: Core and supporting microservices architecture
  • AI: LLM integration, prompt engineering, fine-tuning
  • Data: Storage, schema, persistence, migrations
  • Integrations: External system connections (GitHub, JIRA, etc.)
  • Infrastructure: Deployment, scaling, and operations
  • Workflow: Business process modeling and execution
  • Security: Authentication, authorization, data protection
  • Performance: Optimization, scaling, and monitoring
  • Troubleshooting: Logging, debugging, error handling

Each section contains detailed specifications, architectural guidelines, and design patterns to ensure consistent implementation across the system.