Most teams using coding agents make the same mistake.

They think the prompt is the instruction.

It is not.

For throwaway experiments, a prompt might be enough. For production work, it is not even close.

The real instruction is the specification:

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

When that is missing, coding agents do exactly what you asked for in the moment and quietly violate everything you forgot to say.

That is why AI-assisted coding often feels fast for a while and then starts getting expensive.

The problem is not that the model is dumb.

The problem is that the intent is underspecified.

Why vibe coding breaks down

Vibe coding is useful for exploration.

If you are testing an idea over a weekend, building a throwaway internal tool, or trying to understand a problem space quickly, a loose prompt can be enough.

But production systems accumulate constraints:

  • existing architecture
  • naming conventions
  • error handling patterns
  • permission boundaries
  • contracts with other services
  • latency expectations
  • operational responsibilities

A coding agent only sees what is in context. If the context is a loose prompt plus the current file, it will optimize for local completion, not system coherence.

That is how teams end up with code that works in isolation and breaks in combination.

The first few commits look fast. The tenth change is where the cost shows up.

The real problem: 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. It is not.

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 delivery success?

What should the API return on validation failure?

Should the service support multi-tenant isolation now or later?

If those decisions are not written down, the agent fills the gaps. And once the agent fills them, the code starts encoding accidental architecture.

That is the hidden cost of underspecified AI-assisted development.

The spec is the real prompt

For production work, the useful prompt is not "build X."

The useful prompt is a structured spec that tells the agent what problem it is solving, what boundaries it must respect, and what success looks like.

A good spec does three things:

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

That is why the spec is the real prompt.

Not because it sounds process-heavy. Because it is the only reliable way to align human intent with machine execution once the system matters.

What a useful spec actually 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

This does not tell the agent every implementation detail.

That is the point.

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 becomes subjective:

  • does this look fine?
  • does the structure feel okay?
  • did the model do something weird?

With a spec, code review becomes sharper:

  • does the implementation satisfy the contract?
  • did it add unsupported behavior?
  • did it ignore an acceptance criterion?
  • did it violate an explicit constraint?

That shift matters.

It moves the team from reviewing raw code generation to reviewing conformance against intent.

That is a much better way to work with coding agents.

The workflow that actually works

This is the sequence we have found most reliable:

1. Explore fast

Use loose prompts early to explore the problem space.

This is where vibe coding is actually useful:

  • quick spikes
  • throwaway experiments
  • understanding APIs
  • 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 transition point is usually obvious:

  • new changes start breaking existing behavior
  • different contributors need shared understanding
  • the code touches real workflows or real users
  • the system needs non-trivial review or operational ownership

That is when exploration should end and explicit intent should begin.

3. Break the work into reviewable tasks

Once the spec exists, implementation should be chunked:

  • small tasks
  • narrow diffs
  • explicit dependencies
  • verifiable outputs

This is where spec-driven workflows help. The value is in making the spec the maintained artifact instead of relying on increasingly vague chat history.

4. Verify against the spec, not against vibes

Every meaningful acceptance criterion should map to a check:

  • tests
  • contract checks
  • integration behavior
  • review criteria

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

Why this matters more with agents than without them

Humans can often compensate for ambiguity through conversation, intuition, and institutional memory.

Coding agents cannot.

They fill gaps with plausible decisions.

Sometimes those decisions are good.

Sometimes they are subtly wrong in ways that only show up later:

  • the wrong abstraction becomes sticky
  • a missing constraint turns into production behavior
  • one API contract drifts from another
  • operational edge cases are never handled because nobody asked for them explicitly

That is why underspecification becomes more expensive when implementation gets faster.

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

The practical takeaway

Do not abandon vibe coding.

Use it for what it is good at: exploration.

But once the work matters, stop pretending the prompt is enough.

Write the spec.

Make it short if needed. Make it ugly if needed. But make the intent explicit enough that the agent is executing against a contract rather than improvising architecture.

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

At CoEdify, we use coding agents as part of a disciplined engineering workflow. For real product work, the winning pattern is simple: explore fast, freeze intent, implement in checked slices, and review against an explicit spec. [coedify.com]