Prompt-to-production gap: how we use AI agents to ship reliable software (without losing engineering fundamentals)

Intro

Most vibe coding workflows stop at one milestone: “it runs.” Production starts where that milestone fails.

Generated code that “works” can still:

• violate permission boundaries
• introduce cross-tenant data leaks
• create tightly coupled logic that breaks under iteration

To ship real software, generated code must meet a higher bar: correctness, maintainability, and security.

This is where many teams hit friction.

In this post, we share the process we use to close the prompt-to-production gap—turning AI agents from rapid prototyping tools into reliable contributors to production systems.

By “AI agents,” we mean LLM-driven tools that iteratively generate, modify, and test code within a structured workflow, e.g. Claude Code, OpenAI Codex.

Why this matters

We built ConeShare: an open-source, self-hosted solution for secure document sharing and automated workflows

We’ve used AI-assisted workflows to build core features such as:

• dataroom permissions
• share-link security controls
• activity automation
• ...

AI significantly accelerated our feature delivery speed, but it also surfaced reliability and quality problems when constraints were weak.

We operate in security-sensitive, multi-tenant paths, where small mistakes can lead to:

• unintended data exposure
• broken permission boundaries
• inconsistent cross-service behavior

For example, an early AI-generated authentication change introduced cross-tenant data access due to a subtle scoping issue.

That kind of failure makes one thing clear: speed without constraints does not translate to reliability.

The process below is what helped us change that.

1) Structured planning before implementation

We do not allow agents to jump directly into coding.

Instead, we enforce a planning hierarchy:

• docs/strategy/ — long-lived system design, principles, and rationale

• docs/features/ — feature-level architecture, boundaries, and flows

• plans/ — step-by-step implementation plans

  • e.g. define verification contract → model + migration → failing tests → service + API → integration → regression tests

• docs/development-log.md — decisions, tradeoffs, and implementation history

Why this works:

• Strategy remains stable while implementation evolves
• Plans translate architecture into executable, verifiable steps
• Agents operate within predefined intent instead of improvising structure

Without this layer, agents tend to:

• skip implicit requirements
• produce inconsistent abstractions
• generate partially aligned implementations across files

This structure ensures generated code follows design—not the other way around.

2) Test-driven development as a control mechanism

TDD is a best practice in software engineering and has been validated for decades.

In practice, many teams still have limited tests because writing and maintaining test code adds real overhead.

AI changes that economics: test code becomes much cheaper to produce and iterate. That makes disciplined TDD practical at a much broader scale.

We constrain agents to small, test-driven increments.

Key practices:

• Test isolation — clean state, scoped fixtures

• Mocked dependencies — email, storage, third-party services

• Coverage across:

  • models and relationships
  • API behavior and permissions
  • security flows (auth, verification, reset)
  • data scoping and isolation

TDD here is not just validation—it is a control mechanism for agent behavior.

It:

• prevents large, unverified patches
• limits scope drift
• forces explicit contracts before implementation
• reduces hallucinated integrations between components

Instead of asking “does this code work?”, we force a tighter loop: “what must be true before this code is allowed to exist?”

Example: password reset tokens

A typical prompt might be:

“Implement password reset”

An agent will generate something that works. But critical constraints are often implicit and easy to miss:

• tokens must expire after a fixed time
• tokens must be single-use
• tokens must be scoped to the correct user (and tenant)
• invalid or expired tokens must not leak information

In our workflow, these are defined before implementation and encoded as tests.

Only then do we allow the agent to generate code.

This shifts the task from:

“make password reset work”

to:

“satisfy these non-negotiable security invariants”

This is the difference between code that appears correct and code that is constrained to be correct.

3) Modular architecture for iterative development

Modular architecture has a central role in software engineering because it directly affects development velocity, maintainability, and long-term system health.

AI agents are significantly more reliable when system boundaries are explicit and modular architecture is what enforces those boundaries.

Without explicit constraints, AI tends to generate shallow modules:

• too many exposed interfaces
• fragmented logic spread across many small files
• weak internal cohesion

This makes it harder for an AI agent to track dependencies, reason about the architectural graph, and establish clean test boundaries.

What we want instead is deep modules—units with clear ownership and meaningful internal depth:

• simple, narrow interfaces
• strong encapsulation of complex, related internal behavior
• clearer ownership and tighter test scope

We enforce this through a small set of structural rules:

Separation of concerns

Each domain module owns its:

• models
• APIs
• business logic

Event-driven communication

Cross-module interactions follow a strict direction:

• Upper layers may call downward
• Lower layers communicate upward only through explicit events (e.g., user_created → onboarding workflow)

Why this matters for AI-generated code ?

Without clear boundaries, agents tend to:

• reach across modules
• duplicate logic
• couple unrelated domains

A common failure mode: an agent directly calls internal logic from another module, bypassing its interface. This creates hidden dependencies that don’t fail immediately but break under iteration.

In ConeShare, this structure allows permission models, share-link validation, and frontend access behavior to evolve independently while still coordinating through explicit contracts.

The result is a system that tolerates iteration—without accumulating fragile coupling.

What this changed for us

Adopting these constraints led to:

• fewer regressions from generated changes
• higher-signal code reviews (changes match planned scope)
• easier debugging and rollback via decision history
• faster onboarding for both engineers and agents

Before this, we allowed larger, loosely guided generations. That accelerated early progress but introduced subtle, compounding failures.

The shift was not about slowing down. It was about making iteration safe.

If you are vibecoding this week, start here

A lightweight sequence:

1. Co-create a one-page feature plan (goal, scope, non-goals, risks)
2. Ask for a minimal, ordered implementation plan
3. Write tests first for high-risk areas (auth, permissions, data isolation)
4. Keep patches small (single concern per change)
5. Enforce module boundaries (no cross-domain shortcuts)
6. Review every diff for security and tenancy assumptions
7. Maintain a development log for future context
8. Treat “tests pass” as a gate, not proof of correctness

This reduces rework, hidden coupling, and “mystery bugs.”

Conclusion

The key insight is simple:

Constraints—not speed—enable reliable AI-assisted development.

Planning, testing, and architecture are not overhead. They are what make AI-generated code trustworthy.

Closing the prompt-to-production gap is not about generating more code.

It is about ensuring what gets generated can be:

• understood
• verified
• maintained
• safely extended

This is what turns AI from a fast generator into a reliable engineering tool.

ConeShare is open source and built in public if you want to see this approach applied in practice.