The Inevitable Shift: From Static Documents to Executable Specifications

For the last thirty years, the fundamental unit of product definition has been the document. Whether it was a traditional spec in Word, a Google Doc PRD, or a Jira ticket, the assumption was: Humans write requirements for humans.

That assumption collapses in the era of Agentic AI. Today, the primary consumer of a Product Requirement Document (PRD) is no longer just a developer — it is a Large Language Model (LLM) tasked with generating, modifying, and validating code.

This forces a redefinition of what a requirement is. We are shifting from Dead Text → Machine-Readable Specifications, and teams using the Model Context Protocol (MCP) will gain a fundamental advantage by eliminating ambiguity and drift.

Phase 1: The Failure of “Dead Text”

The traditional PRD is what we call Dead Text — a snapshot of intent that immediately begins to rot. Ambiguity is baked into natural language. A PM writes: "The user should be able to upload a profile picture easily."

For a human developer, “easily” might imply standard UX patterns. For an AI agent, “easily” is a null value — it contains zero constraints: file size limits, format validation, aspect ratio behavior, or error handling.

When an AI attempts to code from Dead Text, it hallucinates the missing details, leading to feature drift. The code "works," but the product feels wrong.

Phase 2: Structure — The First Revolution (ProdMoh)

The industry’s next step was structure. Tools like ProdMoh moved requirements into fields like User Stories, Acceptance Criteria, Constraints, and Dependencies.

This improved clarity — but the “air gap” remained. Developers still had to switch tools, interpret text manually, and translate it into code.

Phase 3: The Breakthrough — MCP and Executable Specs

MCP closes the air gap. It turns a PRD into a live API endpoint that an IDE or AI agent can query.

Just as a browser reads HTML to render a UI, an Agentic IDE reads a ProdMoh PRD (via MCP) to generate application logic with the correct constraints.

How MCP streams product requirements directly into the IDE context loop.

How It Works (Real Example)

1. PM Input: Story defined in ProdMoh with constraints like Max size: 5MB, format: JPG/PNG, aspect ratio: 1:1
2. IDE Connection: Developer authenticates with their ProdMoh MCP token.
3. Developer Prompt: “Implement image uploader.”
4. Agent Action: IDE agent calls get_story_context() and fetches the exact constraints — no guessing.

The agent generates validation logic using the exact rules the PM wrote. There is no “interpretation layer.” No drift. No translation errors.

The New Role of the Product Manager

This does not replace PMs — it elevates them. In the Agentic era, Product Managers become System Architects of Intent.

In the past, vague acceptance criteria could be rescued by an engineering lead. In the future, ambiguity becomes defective code at the speed of light.

If it is not in the MCP stream, it does not exist to the agent.

Conclusion: The Living Spec

MCP transforms static documentation into a living, executable specification.

When a PRD is updated in ProdMoh, the agent in the IDE knows instantly. Iteration cycles collapse. Feature slippage disappears.