Five copies of Claude, running simultaneously and talking to each other through inter-process communication (IPC - a method for separate programs to exchange messages on the same machine), just built a terminal-based YubiKey manager from scratch. The whole thing was captured on video.
The project, called Claude-IPC, assigns each Claude instance a different role in the development process. Rather than one AI agent trying to handle everything, the instances divide the work - architecture, implementation, testing, code review - and coordinate their efforts through message passing. The result is a functional command-line tool for managing YubiKey hardware security keys.
This is a pattern worth paying attention to. Single-agent AI coding has clear limits: context windows fill up, the model loses track of earlier decisions, and complex projects stall out. Multi-agent setups like this sidestep those problems by keeping each instance focused on a narrower task. One Claude handles the overall architecture while another writes tests, and they sync up through structured messages rather than trying to hold the entire project in one context.
The practical output here - a YubiKey manager - is less interesting than the coordination mechanism. IPC is a well-understood concept in software engineering, but applying it to AI agents working on the same codebase is still early-stage territory. Most multi-agent coding demos use orchestration frameworks or API calls between agents. Running multiple Claude instances locally and having them communicate through standard IPC channels is a more lightweight approach that any developer with API access could replicate.
The demo is rough around the edges, as you would expect from a proof of concept. But it shows that multi-agent AI development does not require complex infrastructure. Five Claude instances, a communication protocol, and clear role definitions produced working software. For developers already using Claude Code or similar AI coding tools, this points toward a near future where spinning up a small team of AI agents for a project is as routine as opening multiple terminal tabs.