AI-Agents: A Practical Guide to Building a Complete AI Software Development Team

AI-Agents is a practical guide project for Windows users, designed to help developers and AI enthusiasts build an AI software team with planning, coding, and review capabilities. This project adopts a team-based agent structure, supports mainstream models like Claude and OpenAI, and allows users to get started quickly without deep technical background, experiencing the efficiency improvement brought by multi-agent collaboration.

AI-Agents is positioned as a guide for Windows users to build AI agent teams, aiming to help build fully functional AI agent software teams. Unlike complex development frameworks, it adopts a step-by-step learning path, allowing non-technical users to get started easily. The core concept is to organize AI agents into a structure similar to a real software team, where each agent takes on a clear role (e.g., planning, coding, review), completes complex tasks through collaboration, improves output quality, and makes the process more manageable.

AI-Agents uses a team-based workflow design with clear responsibilities for each agent: the planning agent is responsible for understanding requirements, breaking down tasks, and formulating execution plans; the coding agent generates code and implements functional modules based on the plan; the review agent checks code quality, identifies potential issues, and provides improvement suggestions. This project is suitable for scenarios such as software planning, code generation, task review, prompt engineering workflows, and team-based AI assistance. It also natively supports the Model Context Protocol (MCP), allowing calls to external tools and access to file data.

AI-Agents is designed as a model-agnostic framework, supporting multiple mainstream large language models: Anthropic's Claude series (long context and reasoning capabilities), OpenAI GPT series (e.g., GPT-4, GPT-4o), and other LLM tools that comply with API specifications. Users can flexibly choose the underlying model based on task characteristics, cost, and performance requirements.

System Requirements: Windows 10/11, stable internet connection, sufficient disk space, modern browser (Edge/Chrome/Firefox). Installation Steps: 1. Download the Windows version installation package (.exe/.msi/.zip); 2. Unzip or run the installation wizard; 3. First-time configuration: Enter API key, select model version, set workspace folder, configure agent roles and MCP tools. Recommended Workspace Structure: Under Workspace/, there are Projects (development projects), Prompts (prompt templates), Outputs (generated files), Logs (run logs), Notes (personal notes).

Basic Usage Flow: Launch the application → Create/select a task → Assign agent roles → Enter prompts/goals → Execute workflow → Review output → Save/export. Best Practices for Prompt Engineering: Keep it concise and clear (one task at a time), set clear goals, use everyday language, and iterate for optimization. Beginners can try tasks like reviewing code errors, writing functional plans, explaining script logic, and suggesting folder structures.

Application fails to start: Confirm file integrity, run as administrator, unblock Windows restrictions, check version correctness. Unable to connect to model service: Verify API key correctness/validity/balance, check network connection, confirm model name. File save failure: Check write permissions for the target folder, change workspace directory, ensure the folder is not read-only.

AI-Agents is suitable for the following groups: people who want to apply AI to development, users seeking guided agent configuration, developers needing team collaboration workflows, prompt engineering practitioners, and users comparing multiple models. The project lowers the entry barrier for AI agent technology and provides a practical multi-agent collaboration solution. As LLM capabilities improve and multi-agent mechanisms mature, such tools will play a more important role in the software development field.