Building a Multi-Agent ATDD Pipeline with Hexagonal Architecture
📰 Dev.to · Carlos Eduardo Sotelo Pinto
Learn to build a multi-agent ATDD pipeline with hexagonal architecture, enabling autonomous testing and development with AI agents
Action Steps
- Design a hexagonal architecture for the ATDD pipeline to enable flexibility and scalability
- Implement a swappable execution engine to coordinate AI agents and tasks
- Develop specialized AI agents to write tests, implement code, and verify quality
- Integrate the AI agents with the execution engine and ATDD pipeline
- Configure the pipeline to run acceptance scenarios and report results
Who Needs to Know This
This benefits dev teams and QA engineers by automating testing and development, improving efficiency and reducing manual errors. It requires collaboration between developers, QA engineers, and AI/ML experts to design and implement the pipeline
Key Insight
💡 Hexagonal architecture enables a flexible and scalable ATDD pipeline, while AI agents automate testing and development tasks
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🤖 Automate your ATDD pipeline with AI agents and hexagonal architecture! 💻
Key Takeaways
Learn to build a multi-agent ATDD pipeline with hexagonal architecture, enabling autonomous testing and development with AI agents
Full Article
How I built a fully autonomous ATDD pipeline where specialized AI agents write tests, implement code, verify quality, and run acceptance scenarios — coordinated by a swappable execution engine behind a clean port.
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