Inspiration
Modern software engineering is collaborative, but AI coding is often limited to one agent at a time. I wanted to explore whether a manager agent could coordinate multiple specialist agents in parallel, each working towards the same goal.
What it does
A multi-agent orchestration framework built on top of codex-app-server. A manager agent can spawn, monitor, and steer multiple child agents across different models and tasks, enabling parallel implementation, testing, code review, research, and debugging.
How I built it
Built using codex-app-server as the execution layer, with an orchestration framework that manages agent lifecycles, task assignment, progress tracking, model selection, and communication between manager and worker agents.
Challenges I ran into
The hardest part wasn't creating agents—it was coordinating them. Avoiding duplicate work, managing context, handling long-running sessions, and deciding when the manager should intervene were the biggest challenges.
Accomplishments that I'm proud of
Creating a flexible orchestration framework that supports parallel AI workflows instead of isolated chats, while remaining model-agnostic and extensible for future capabilities.
What I learned
Effective orchestration is just as important as model capability. Multiple specialised agents with clear responsibilities often outperform a single general-purpose agent.
What's next for Multi-Agent Orchestration
Next steps include richer dashboards, shared memory, automatic task planning, smarter model selection, conflict resolution, and support for orchestrating even more AI coding agents and tools.
Built With
- codex
- python