multi-agent

To coordinate multiple AI agents on a shared codebase in parallel, you need a structured workflow with a docs-as-bus communication pattern using shared artifacts and explicit interfaces to synchronize development streams and manage dependencies.

The docs-as-bus pattern for agent coordination uses structured files in a shared artifacts directory to manage handoffs, traceability, and synchronization between parallel agents, replacing direct messaging with a documented communication channel.

To prevent conflicting changes when running parallel AI agents, define explicit interface contracts and guardrails across modules, and use integration checkpoints to handle dependencies and ensure smooth integration of parallel development streams.

Multi-agent orchestration can manage backend, frontend, and tests simultaneously by distributing work across parallel agents and coordinating end-to-end module execution through a shared artifacts directory to reduce cycle time on complex projects.

To start orchestrating parallel agents for a complex codebase, define a task plan and assign agents to execute specific modules while coordinating handoffs and integration checks via a shared artifacts directory.

You should not use parallel execution for AI code generation on simple, single-module projects where serial task execution is sufficient, as parallel agent orchestration requires a defined artifact workflow and explicit interface contracts to be effective.