Most teams using coding agents make the same mistake: they treat the prompt as the instruction.

For a throwaway experiment, a prompt can be enough. For production work, the real instruction is the specification:

  • what the system should do
  • what it should not do
  • how success is measured
  • which interfaces and constraints must hold
  • what failure looks like

When that is missing, a coding agent does exactly what you asked for in the moment and quietly breaks everything you forgot to say.

That is why AI-assisted coding often feels fast for a while and then gets expensive. The model is usually capable enough. The intent is underspecified.

Why vibe coding breaks down

Vibe coding, where you prompt loosely and keep whatever works, is useful for exploration. If you are testing an idea over a weekend, building a throwaway internal tool, or learning a problem space, a loose prompt is fine.

Production systems pile up constraints:

  • existing architecture
  • naming conventions
  • error handling patterns
  • permission boundaries
  • contracts with other services
  • latency expectations
  • who owns the system in production

A coding agent only sees what is in its context. If that context is a loose prompt plus the current file, the agent optimizes for finishing the local task. Keeping the whole system coherent is outside its view.

That is how teams end up with code that works alone and breaks in combination. The first few changes feel fast. Around the tenth change, the cost shows up.

The agent cannot see your intent

Here is the typical workflow:

Developer writes prompt -> Agent generates code -> Developer scans diff -> Commit

The prompt might be:

Build a real-time notification service with WebSocket support.

That sounds clear, but it leaves most of the decisions open:

  • What does "real-time" mean?
  • What are the authentication rules?
  • What happens when Redis is down?
  • How many queued notifications can a user hold?
  • What counts as successful delivery?
  • What should the API return when validation fails?
  • Should the service support multi-tenant isolation now or later?

If those decisions are not written down, the agent makes them. Once it does, the code starts to encode architecture nobody chose. That is the hidden cost of underspecified AI-assisted development.

The spec is the real prompt

For production work, the useful prompt is a structured spec. It tells the agent what problem it is solving, which boundaries it must respect, and what success looks like.

A good spec does three things:

  1. It removes ambiguity before implementation.
  2. It stops the agent from silently inventing architecture.
  3. It gives the reviewer a contract to check the output against.

Writing one can feel like process overhead. Once the system matters, it is the most reliable way to line up what you meant with what the agent builds.

What a useful spec looks like

A good spec does not need to be long. It needs to be precise. Here is the kind of implementation spec that works well with coding agents:

# Notification Service - Implementation Spec

## In Scope
- WebSocket-based notifications for authenticated users
- POST /notifications endpoint
- Per-user queue with max 100 messages
- Delivery acknowledgement

## Out of Scope
- Mobile push notifications
- Email notifications
- File attachments
- Multi-region delivery

## Interface Contract
- POST /notifications accepts user_id, type, title, body, priority
- Returns 201 on success
- Returns 400 for validation failure
- Returns 429 when rate limit is exceeded

## Constraints
- Max 100 queued notifications per user
- Max 50 websocket connections per user
- Rate limit: 10 notification requests per minute per user

## Error Handling
- If Redis is unavailable, queue in memory temporarily
- If websocket drops, client reconnects
- Validation errors return structured field-level messages

## Acceptance Criteria
- Valid requests return 201
- Invalid requests return 400 with field detail
- Rate limit violations return 429
- Delivery acknowledgement is recorded

It does not tell the agent every implementation detail, and that is deliberate. It tells the agent enough to stop improvising on the parts that should not be improvised.

What changes once you work this way

Without a spec, code review is subjective:

  • Does this look fine?
  • Does the structure feel okay?
  • Did the model do something weird?

With a spec, review gets sharper:

  • Does the implementation satisfy the contract?
  • Did it add behavior nobody asked for?
  • Did it miss an acceptance criterion?
  • Did it break an explicit constraint?

The team moves from judging generated code by feel to checking it against written intent. That is a much better way to work with coding agents.

The workflow that works

This is the sequence we have found most reliable. It is also how we build features at CoEdify: a written plan of what the feature must do and how we will know it works, before any code is written.

1. Explore fast

Use loose prompts early to explore the problem. This is where vibe coding earns its place:

  • quick spikes
  • throwaway experiments
  • understanding an API
  • proving the model can do something interesting

At this stage, speed matters more than structure.

2. Freeze the intent

Once the problem looks real, stop prompting loosely and write the spec. The moment is usually obvious:

  • new changes start breaking existing behavior
  • several people need a shared understanding
  • the code touches real workflows or real users
  • the system needs serious review or someone to own it in production

That is when exploration ends and written intent begins.

3. Break the work into reviewable tasks

Once the spec exists, split the implementation into:

  • small tasks
  • narrow diffs
  • explicit dependencies
  • outputs you can verify

The spec becomes the document you maintain, in place of a chat history that gets longer and vaguer over time.

4. Check against the spec, not against vibes

Every acceptance criterion that matters should map to a check:

  • tests
  • contract checks
  • integration behavior
  • review criteria

If the output does not match the spec, either the implementation is wrong or the spec is incomplete. Both are fixable, and at least the failure is visible.

Why this matters more with agents

People can often work around ambiguity through conversation, intuition, and what they remember about the system. Coding agents cannot. They fill gaps with plausible decisions.

Sometimes those decisions are good. Sometimes they are wrong in ways that only show up later:

  • the wrong abstraction sticks
  • a missing constraint turns into production behavior
  • one API contract drifts away from another
  • edge cases in operations are never handled because nobody asked for them

The faster the agent writes, the more expensive missing intent becomes.

The practical takeaway

Keep vibe coding for what it is good at: exploration.

Once the work matters, write the spec. Make it short. Make it ugly if you have to. But make the intent explicit enough that the agent works against a contract instead of improvising architecture.

That is the difference between AI-assisted speed and AI-assisted drift.

At CoEdify, we use coding agents inside a disciplined engineering workflow: explore fast, freeze intent in a spec, build in checked slices, and review against the spec.