adlc-product-implement

You can generate a PRD from Product Decision Records by using a multi-agent DAG workflow that transforms PDRs into a unified PRD. The process treats PDRs as the source of truth to derive a centralized, auditable product specification.

A DAG-based workflow for product documentation orchestrates PRD generation through a directed acyclic graph of AI agents. It processes PDRs across Plan, Execute, and Summarize phases to ensure end-to-end coverage and traceable decisions.

To maintain traceability between product decisions and requirements, use PDRs as the canonical source of truth within a DAG-driven workflow. This maps each decision directly into the unified PRD, ensuring full auditable specification coverage.

Yes, you can resume PRD generation across AI agent sessions because the workflow features state persistence and resumable, fault-tolerant operation. This ensures the phase-driven execution continues smoothly from where it stopped.

Conflicting product decisions are resolved during PRD generation by treating the canonical Product Decision Records as the absolute source of truth. The workflow applies PDR lifecycle management to enforce consistency throughout the unified specification.

The best way to structure a PRD from multiple decision drafts is applying a phase-driven workflow of Plan, Execute, and Summarize. This DAG-based approach consolidates various PDR drafts into a single canonical PRD.md file with state persistence.