This new research introduces Agyn, an open-source multi-agent platform that models software engineering as a team-based organizational process rather than a monolithic task.
The system configures a team of four specialized agents: a manager, researcher, engineer, and reviewer. Each operates within its own isolated sandbox with role-specific tools, prompts, and language model configurations. The manager agent coordinates dynamically based on intermediate outcomes rather than following a fixed pipeline.
What makes the design interesting?
Different agents use different models depending on their role. The manager and researcher run on GPT-5 for stronger reasoning and broader context. The engineer and reviewer use GPT-5-Codex, a smaller code-specialized model optimized for iterative implementation and debugging. This mirrors how real teams allocate resources based on task requirements.
The workflow follows a GitHub-native process. Agents analyze issues, create pull requests, conduct inline code reviews, and iterate through revision cycles until the reviewer explicitly approves. No human intervention at any point. The number of steps isn't predetermined. It emerges from task complexity.
The system configures a team of four specialized agents: a manager, researcher, engineer, and reviewer. Each operates within its own isolated sandbox with role-specific tools, prompts, and language model configurations. The manager agent coordinates dynamically based on intermediate outcomes rather than following a fixed pipeline.
What makes the design interesting?
Different agents use different models depending on their role. The manager and researcher run on GPT-5 for stronger reasoning and broader context. The engineer and reviewer use GPT-5-Codex, a smaller code-specialized model optimized for iterative implementation and debugging. This mirrors how real teams allocate resources based on task requirements.
The workflow follows a GitHub-native process. Agents analyze issues, create pull requests, conduct inline code reviews, and iterate through revision cycles until the reviewer explicitly approves. No human intervention at any point. The number of steps isn't predetermined. It emerges from task complexity.