Product Architecture • December 2025

Before/After Examples: Transforming a Traditional PRD into an Agentic PRD

PRDs were originally created for humans. Agentic AI requires something different: precision, structure, and machine-readable constraints. This guide shows real before/after examples of how vague, prose-based requirements convert into structured, test-ready Agentic PRDs suitable for MCP-powered engineering workflows.

Why This Matters

Agentic engineering workflows rely on a continuous loop: Product Intent → Machine-readable Specification → AI Code Generation → Verification → Deployment.

Traditional PRDs break this loop. They are ambiguous, unstructured, and written for meetings — not machines.

Key Insight

The Agentic PRD is the “source code of product intent.” It must be structured, explicit, and machine-parsable.

Below are practical, real-world before/after conversions showing exactly how to rewrite PRDs for an AI-driven engineering environment powered by ProdMoh + MCP.

Example 1 — “Paid Badge” on Product Listings

❌ Traditional PRD Version (Before)

The original human-written requirement looked like this:

Users should see a “Paid” badge on items that are paid.  
If the item is free, don’t show the badge.  
Make it work for all product types.

This PRD is short, understandable — and dangerously ambiguous for an LLM or agent.

Why It Fails for AI

✅ Agentic PRD Version (After)

Rewrite using structured fields (as supported by ProdMoh + MCP):

{
  "id": "S-100",
  "title": "Display paid badge on priced items",
  "intent": "Show a 'paid' badge on all items where price > 0.",
  "acceptance": [
    {"type": "predicate", "expr": "product.price > 0 implies badges includes 'paid'"},
    {"type": "predicate", "expr": "product.price == 0 implies badges excludes 'paid'"},
    {"type": "predicate", "expr": "product.price < 0 is invalid_input"}
  ],
  "examples": [
    {
      "product": {"id":"p-123", "price":199},
      "expected": {"badges":["paid"]}
    },
    {
      "product": {"id":"p-456", "price":0},
      "expected": {"badges":[]}
    }
  ],
  "meta": {"version": "2025.11.1", "author": "pm@company.com"}
}

Why It Works

Outcome

MCP-connected IDE generates complete Jest tests and component logic with zero hallucination.

Example 2 — “User Should Upload a Profile Picture Easily”

❌ Traditional PRD Version (Before)

Allow users to upload a profile picture easily.  
Support all common formats.  
Show appropriate error messages when upload fails.

Issues

✅ Agentic PRD Version (After)

{
  "id": "S-210",
  "title": "Profile picture upload with validations",
  "intent": "Enable upload of profile images with strict validation rules.",
  "acceptance": [
    {"type":"predicate","expr":"file.size <= 5MB"},
    {"type":"predicate","expr":"file.format in ['jpg','jpeg','png']"},
    {"type":"predicate","expr":"image.aspect_ratio == 1:1"},
    {"type":"predicate","expr":"invalid_format implies error.code == 'UNSUPPORTED_FORMAT'"},
    {"type":"predicate","expr":"too_large implies error.code == 'FILE_TOO_LARGE'"}
  ],
  "examples": [
    {
      "file": {"format": "png", "size": 4210000},
      "expected": {"valid": true}
    },
    {
      "file": {"format": "gif", "size": 30000},
      "expected": {"error": {"code":"UNSUPPORTED_FORMAT"}}
    }
  ],
  "meta": {"version":"2025.11.5"}
}

Why It Works

Example 3 — “Checkout Should Be Fast”

❌ Traditional PRD Version (Before)

The checkout flow should be fast and should not time out even during peak traffic.

Issues for AI

✅ Agentic PRD Version (After)

{
  "id": "S-315",
  "title": "Checkout latency constraints",
  "intent": "Ensure checkout response times meet performance budgets.",
  "acceptance": [
    {"type":"nfr","expr":"p95_latency <= 220ms"},
    {"type":"nfr","expr":"p99_latency <= 350ms"},
    {"type":"nfr","expr":"error_rate <= 0.1%"},
    {"type":"predicate","expr":"timeout implies alert('checkout_timeout')"}
  ],
  "examples": [],
  "meta": {"version":"2025.12.1"}
}

Why It Works

Note

Agentic PRDs don’t require examples for NFRs — but they require strict numeric boundaries.

Example 4 — “Users Should See Recommended Products”

❌ Traditional PRD Version (Before)

Show recommended items based on user behavior.

Issues

✅ Agentic PRD Version (After)

{
  "id": "S-440",
  "title": "Product recommendations based on viewing history",
  "intent": "Show contextually relevant product recommendations.",
  "acceptance": [
    {"type":"predicate","expr":"recommendations.count == 6"},
    {"type":"predicate","expr":"recommendations[i].score >= 0"},
    {"type":"predicate","expr":"empty_history implies recommendations.count == 0"},
    {"type":"predicate","expr":"invalid_product implies error.code == 'INVALID_INPUT'"}
  ],
  "examples": [
    {
      "history": ["p1","p2"],
      "expected": {"recommendations":[{"id":"r1","score":0.78}]}
    }
  ],
  "meta":{"version":"2025.10.7"}
}

Why This Works

The 7-Step Framework for Converting Any PRD into an Agentic PRD

Use this template for rewriting any requirement:

  1. Identify intent — What outcome must be achieved?
  2. Extract constraints — Size limits, format rules, boundaries.
  3. Convert prose → predicates — Logical expressions.
  4. Add positive examples — Valid inputs.
  5. Add negative examples — Invalid or boundary inputs.
  6. Define error contracts — What errors should be thrown?
  7. Version it — For source-of-truth traceability.
Remember

If it’s not in the PRD, the AI agent won’t enforce it. Ambiguity becomes hallucination. Precision becomes automation.

Best Practices When Writing Agentic PRDs

Conclusion

The shift from traditional PRDs to Agentic PRDs is not just a formatting change — it’s a structural transformation in how organizations encode and transmit product intent.

With ProdMoh + MCP, teams move from “describing” requirements to **operationalizing specifications** that AI agents can use directly to generate tests, code, and validation steps.

The result is predictable engineering, fewer clarifications, faster delivery, and near-zero drift between product intent and shipped behavior.

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