DevTeamAI

A multi-agent software development platform where AI product managers, architects, engineers, QA, and CTOs collaborate to turn ideas into production-ready solutions.

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DevTeam AI

Inspiration

Software development is a collaborative process, yet most AI coding assistants work in isolation. A single prompt often tries to replace an entire engineering team, resulting in generic outputs, missed requirements, architectural inconsistencies, and little opportunity for review or iteration.

We wanted to explore a different approach.

Our inspiration came from observing how high-performing engineering teams operate. Product Managers define requirements, Architects design scalable systems, Engineers implement features, QA validates quality, and CTOs challenge assumptions before anything reaches production.

Instead of building another AI chatbot, we built DevTeam AI—a multi-agent software engineering platform where specialized AI agents collaborate, critique each other's work, negotiate trade-offs, and keep humans in control through structured approval workflows.

This vision aligned perfectly with the QwenCloud Agent Society Hackathon, where the focus is not just intelligent agents, but intelligent societies of agents working together.

What it does

DevTeam AI transforms a simple product idea into a comprehensive software delivery plan through an orchestrated team of AI specialists powered by QwenCloud.

The platform includes specialized agents such as:

  • 🎯 Product Manager
  • 🏗️ Solution Architect
  • ⚙️ Backend Engineer
  • 📱 Frontend/Mobile Engineer
  • 🧪 QA Engineer
  • 👨‍💼 CTO Reviewer
  • 🤝 Negotiator
  • 🔄 Revision Coordinator
  • 💻 Code Generator
  • 🔍 Code Reviewer
  • 🎼 Workflow Orchestrator

Instead of producing one large AI response, each agent contributes its expertise while building upon previous outputs.

The platform generates:

  • Product Requirements Document (PRD)
  • System Architecture
  • Mermaid Architecture Diagrams
  • API Specifications
  • Database Schema
  • Frontend Development Plan
  • Backend Development Plan
  • QA Test Strategy
  • Security Recommendations
  • CTO Review Report
  • Conflict Resolution Reports
  • Revision Summaries
  • Starter Project Code
  • Exportable project artifacts

A unique feature of DevTeam AI is its human approval workflow. Users can review, approve, regenerate, or request revisions after every stage before the workflow proceeds.

How we built it

DevTeam AI combines modern web technologies with QwenCloud's powerful language models.

Frontend

  • Next.js 15
  • React
  • TypeScript
  • Tailwind CSS
  • shadcn/ui
  • Server-Sent Events (SSE) for real-time progress

Backend

  • FastAPI
  • Python
  • SQLite
  • SQLAlchemy
  • Pydantic
  • httpx

AI Layer

The platform uses QwenCloud's OpenAI-compatible API as the reasoning engine behind every specialized agent.

Each agent has:

  • its own identity
  • dedicated system prompt
  • role-specific objectives
  • context from previous stages
  • structured outputs

The backend orchestrates the workflow, manages project state, validates dependencies, stores artifacts, and coordinates communication between agents.

We also implemented:

  • Stage locking
  • Approval gates
  • Conflict detection
  • Regeneration workflows
  • Live progress streaming
  • Project history
  • ZIP export of generated artifacts

Finally, we deployed the entire application using Docker, Nginx, and Alibaba Cloud ECS, making the platform publicly accessible.

Challenges we ran into

Building an AI society turned out to be far more difficult than simply calling an LLM multiple times.

Some of our biggest challenges included:

Designing truly specialized agents

Initially, many agents produced similar responses because their responsibilities overlapped.

We spent significant time refining prompts so each agent contributed unique expertise instead of repeating previous outputs.

Agent coordination

Every stage depends on previous work.

We needed a workflow engine that could:

  • prevent stages from running too early
  • handle approvals
  • support revisions
  • maintain project consistency

This required building a dependency-aware orchestration system.

Managing state

Unlike traditional chatbots, DevTeam AI maintains an evolving project state containing:

  • artifacts
  • approvals
  • revisions
  • conflicts
  • discussions
  • generated code

Keeping everything synchronized across multiple agents was one of the hardest engineering problems.

Deployment

Deploying on Alibaba Cloud ECS introduced several infrastructure challenges:

  • Docker networking
  • Nginx reverse proxy configuration
  • HTTPS setup
  • Security Groups
  • container communication
  • API routing

Solving these issues gave us valuable hands-on experience deploying production-style AI applications.

Accomplishments that we're proud of

We're especially proud that DevTeam AI feels less like chatting with an AI and more like collaborating with an actual engineering organization.

Some highlights include:

  • ✅ 11 specialized AI agents working together
  • ✅ Human-in-the-loop approval workflow
  • ✅ Multi-agent collaboration instead of single prompts
  • ✅ CTO review and architectural critique
  • ✅ Negotiation and conflict resolution between agents
  • ✅ Live workflow visualization
  • ✅ Real-time progress updates
  • ✅ Starter code generation
  • ✅ Exportable project documentation
  • ✅ Deployment on Alibaba Cloud ECS
  • ✅ Powered entirely by QwenCloud reasoning

Most importantly, we demonstrated that multi-agent collaboration can produce more structured, transparent, and trustworthy software planning than a single AI conversation.

What we learned

This project changed how we think about AI systems.

Some key lessons include:

  • Multiple specialized agents outperform one general-purpose agent for complex workflows.
  • Human approval significantly improves trust and usability.
  • AI collaboration becomes far more valuable when agents can review and challenge each other's decisions.
  • Prompt engineering is as much about defining responsibilities as writing instructions.
  • Workflow orchestration is just as important as model quality.
  • QwenCloud's OpenAI-compatible API made integrating sophisticated AI capabilities remarkably straightforward, allowing us to focus on designing intelligent workflows instead of infrastructure.

Perhaps the biggest lesson was this:

The future of AI isn't one super assistant doing everything—it's teams of specialized AI agents collaborating the same way humans do.

What's next for DevTeam AI

This hackathon project is only the beginning.

Our roadmap includes:

  • Persistent long-term agent memory using vector databases
  • GitHub repository integration
  • Automatic pull request generation
  • CI/CD planning agents
  • DevOps and Infrastructure agents
  • Security auditing agent
  • Cost estimation agent
  • Product roadmap generation
  • Multi-model agent collaboration
  • Team collaboration with multiple human reviewers
  • Support for additional programming languages and frameworks
  • Cloud deployment automation
  • Enterprise project management integrations

Our long-term vision is to evolve DevTeam AI into a complete AI Software Engineering Platform, where founders, startups, enterprises, and engineering teams can collaborate with an intelligent society of AI agents to move from idea to production faster, with greater quality and confidence.

With QwenCloud providing the intelligence behind every specialized agent, we're excited to continue pushing the boundaries of collaborative AI systems.

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