The Problem With Traditional Copilots — And What Comes Next

The uncomfortable truth about traditional copilots

Tools like GitHub Copilot, Cursor’s early models, and ChatGPT-based IDE plugins transformed productivity — yet they all operate under the same paradigm:

A copilot is essentially a predictive engine. It assists with:

• autocomplete
• boilerplate generation
• refactoring suggestions
• documentation synthesis

But copilots fail at the most important responsibility in software engineering:

Ensuring the implementation aligns with product requirements.

This is the root cause of the biggest engineering failures:

• feature drift
• hallucinated logic
• mismatched assumptions
• broken integrations

Why traditional copilots fail — the 7 systemic limitations

Copilots operate inside the IDE, but product context lives outside it. This architectural gap makes correctness impossible.

1. Copilots don’t know product requirements

No matter how powerful the model is, copilots:

• don’t know acceptance criteria
• don’t know story IDs
• don’t know expected edge cases
• don’t know upstream or downstream constraints

They guess — and guessing in engineering is expensive.

2. Copilots don’t know the business logic

An AI can generate a “plausible” solution that is completely wrong for the business.

3. Copilots can’t verify correctness

Copilots never validate whether:

• this code satisfies the AC
• this output passes the examples
• this function stays within NFR limits

They generate code — they do not test or enforce constraints.

4. Copilots hallucinate when context is missing

// Intended:
// "file size <= 5MB"

Copilot guess:
if (file.size < 20000000) { ... } // <— hallucinated 20MB threshold

Because copilots lack machine-readable product intent.

5. Copilots lack memory and system-level state

If a developer works across files:

• Model A rewrites a function
• Model B overwrites that rewrite
• Model C “fixes” something unrelated

The system drifts — fast.

6. Copilots cannot enforce cross-functional constraints

e.g., Payment team requires:

• Price must never be negative
• Fee rounding must follow bank spec

Copilots do not know any of this.

7. Copilots break at scale

When you have:

• multiple microservices
• cross-team dependencies
• shared schemas
• organizational NFR policies

Copilots become dangerous instead of helpful.

What engineering teams actually need

To move beyond copilots, teams need:

1. Product intent that is machine-readable
2. A standard protocol for delivering requirements into the IDE
3. An agent that plans, executes, and verifies work

This is not autocomplete — this is autonomy.

The next era: From Copilots → Agentic Engineering

Agentic Engineering replaces guesswork with a closed-loop system of intent → code → verification.

Agentic AI uses a control loop (OODA):

• Observe — pull machine-readable PRD via MCP
• Orient — build a plan
• Decide — choose best path
• Act — generate code + tests
• Verify — run tests, compare against PRD
• Self-correct

A copilot performs only the last step. An agent performs all of them.

How Agentic Engineering works (concrete example)

Step 1 — PM creates an Agentic PRD

{
  "id":"S-100",
  "title":"Display paid badge",
  "acceptance":[
    {"type":"predicate","expr":"price > 0 implies badges.includes('paid')"}
  ],
  "examples":[
    {"query":"red shoes","price":199,"badges":["paid"]}
  ]
}

Step 2 — IDE agent requests PRD through MCP

GET /mcp/prd/S-100
Authorization: Bearer <token>

Step 3 — Agent generates the test first

test("badges include paid when price>0", () => {
  const product = { price:199, badges:[] };
  const result = addBadges(product);
  expect(result.badges).toContain("paid");
});

Step 4 — Agent generates correct implementation

function addBadges(product) {
  if (product.price > 0) {
    return { ...product, badges: ["paid"] };
  }
  return product;
}

Step 5 — Agent runs tests and self-corrects

No copilot can do this.

Why PRDs must evolve for agentic systems

Copilot-friendly PRDs look like this:

User should see a paid badge.
Should feel intuitive.

Agentic PRDs must be:

• structured
• semantic
• machine-verifiable
• MCP-accessible

This is the foundation of Agentic PRDs.

MCP is the missing layer between AI Agents and Product Intent

MCP (Model Context Protocol) creates a reliable channel between:

• ProdMoh (PRD authoring)
• IDEs (code generation + tests)
• CI (verification)

MCP guarantees:

• correct scope of requirements
• consistent versioning
• secure access controls
• structured PRD delivery

Copilots have none of these guarantees.

Why copilots cannot evolve into agents

Copilots were architected for in-IDE token prediction. To become agentic, they would require:

• a structured PRD ecosystem
• a standard model-context transport layer
• global system awareness
• stateful planning capabilities
• verification loops and execution sandboxes

These are not incremental improvements — they require a paradigm shift.

The business impact: speed, quality, and alignment

Agentic Engineering drives measurable impact:

Conclusion: The future is not “better copilots” — it is Agentic Engineering

We are entering a world where:

• PRDs become executable
• AI agents generate + verify code
• IDE assistants collaborate with structured product intent
• MCP becomes the transport layer for requirements

Copilots helped engineers type faster. Agents will help teams build software that actually matches product intent.