Automation

Coordinates multiple AI agents on shared tasks with explicit handoff protocols, shared state management, and conflict resolution so parallel work stays coherent. Multi-agent orchestration is more structured than simple parallel dispatch because agents take on distinct roles with explicit dependencies rather than running identical briefs on independent data.

Distributes embarrassingly parallel work across multiple AI agents with clear briefs and crisp handoff protocols, then aggregates their results through a single integrator. This technique maximizes throughput when tasks are independent and the coordination overhead is low, making it ideal for research chunks, file batches, or parallel data processing.

Coordinates multiple AI subagents on slices of a larger plan where each subagent handles a defined scope while a single parent agent retains accountability for integration, quality, and final delivery. This approach is valuable when a single agent working sequentially would be too slow, but you still need coherent end-to-end quality rather than fragmented outputs.

Bootstraps a complete plugin project structure with manifest files, entry points, configuration schemas, and baseline tests so new Codex or editor extensions follow a consistent, reviewable template from day one. This eliminates the setup tax for creating new plugins and ensures every plugin in a codebase shares the same conventions for configuration, logging, and error handling.