pumasi

Parallel development across multiple AI agents is orchestrated by a Claude-driven PM that decomposes software architecture into independent subtasks, coordinating concurrent Codex workers with automated gate checks. This ensures deterministic, auditable outcomes across multi-module projects.

Phase-driven project management for AI agent workflow orchestration is a process where a central PM creates a comprehensive plan, defines data models, and sequences rounds of parallel execution. It enforces strict task signatures and mandatory gate checks for cohesive integration.

To start parallel Codex execution, define a pumasi.config.yaml file specifying your tasks and run the pumasi.sh start script. This initiates the orchestration, allowing Claude to manage context sharing and monitor independent subtasks concurrently.

Yes, this task distribution approach requires the yaml dependency to define task configurations. You need to structure your project boundaries and task signatures in a config file before executing the orchestration script for parallel AI agent development.

Automated integration and quality checks during parallel AI development are handled through gate-driven QA, executing tsc/build/test checks after each round. Consolidated results ensure deterministic outcomes and centralized monitoring across all concurrent subtasks.

You should use Claude as a PM to coordinate Codex workers for multi-module projects requiring distributed coding, strict phase gating, and round-based execution. It is ideal when you need explicit task signatures and automatic integration for deterministic results.