PRD PAL
An AI PRD agent that authors product and business requirements as machine-usable context, then feeds a chain of specialist agents (front-end, API, database, QA) that build the feature and open a pull request for human review.
The Challenge
Requirements that never reach the code
In most organizations, requirements live as prose in documents that quickly fall out of sync with reality. Developers re-interpret them, details get lost in translation, and the same intent is rebuilt differently by each person who touches it.
The handoff from product thinking to working software is where time and fidelity leak. We wanted requirements that machines could act on directly, and a build chain that produced something a human could actually review, not a black box.
The Process
Requirements-to-PR, end to end
PRD PAL treats the PRD as the contract. An AI agent authors the requirements and surrounding context, and that structured artifact drives a chain of specialist agents, each responsible for one layer of the stack, culminating in a pull request.
Author the PRD as context
The PRD agent builds out product and business requirements plus the surrounding context, capturing intent as structured, machine-usable input rather than static prose.
Fan out to specialist agents
Front-end, API, database, and QA agents each build their layer from the single source of truth, coordinated so the pieces fit together.
Open a reviewable PR
The chain produces a pull request, keeping humans in control of the merge and preserving normal code review and approval workflows.
The Solution
One source of truth, five coordinated agents
PRD PAL authors requirements as machine-usable context, then orchestrates five specialist roles (PRD, front-end, API, database, and QA) that build a feature from that single source of truth. The output is a reviewable pull request, so the team gets the speed of automated implementation without giving up human judgment at the merge.
The Results
From intent to pull request
PRD PAL compresses the path from requirements to implementation into a single automated flow, with specialist agents building from one shared definition and humans reviewing the result.