As AI-assisted development matures, I’m seeing single-agent coding workflows hit practical limits around context, parallelism, and task focus. In this session, I explore when and why coordinated teams of AI agents outperform single-agent approaches—and when they do not.

I present a technical comparison of Claude Code Agent Teams, Claude-Flow, and other mainstream agent orchestrators, examining how different coordination models handle task decomposition, shared context, write boundaries, and control flow. Through concrete examples, I show how these design choices affect correctness, throughput, and developer oversight.

I demonstrate how agent teams reshape development across the software lifecycle, from requirements exploration and architecture design to implementation, testing, and operational support. I contrast single-agent and multi-agent workflows to highlight where specialization and parallel reasoning improve outcomes, and where simpler models remain preferable.

Attendees will leave with a clear framework for deciding when to scale AI assistance using agent teams, how to evaluate orchestration tooling, and how to apply these techniques effectively in real-world engineering environments.

Given how quickly this space changes, new products or other recent advances may be mentioned closer to presentation day.