parallel-infrastructure

DAG scheduling computes the topological order of work packages, enabling parallel feature implementation across multiple coding agents. The scheduler coordinates task queues so backend, frontend, and integration packages execute concurrently while strictly respecting their dependency chains.

Consensus synthesis dispatches review prompts to multiple LLM vendors and merges their findings into a unified consensus report. This aggregates diverse perspectives on the same code, producing a single comprehensive review document that highlights agreed issues and resolves conflicting feedback.

Yes, a scope checker verifies that all agents operate within declared write_allow and write_deny boundaries. This prevents unauthorized file modifications during parallel execution, ensuring each agent stays within its designated directory or package scope.

Yes, the workflow requires three Python scripts to be available: dag_scheduler.py, review_dispatcher.py, and consensus_synthesizer.py. These scripts handle topological scheduling, multi-vendor review dispatch, and feedback merging respectively.

The circuit breaker monitors agent heartbeats, enforces retry budgets, and manages failure escalations during parallel execution. If an agent stops responding or exhausts its retries, the handler triggers escalation procedures to prevent the entire DAG workflow from hanging.

Using a DAG scheduler allows dependent work packages to run concurrently while respecting dependencies. By defining work packages for backend, frontend, and integration layers, the scheduler calculates execution order so independent tasks run in parallel and dependent tasks wait.