Stop Vibe Coding Your AI Agents: An Engineering-First Approach

Agentic AI moves fast. A few lines of code, a powerful LLM, and suddenly an agent is doing something that looks impressive. The rapid iteration is addictive, but it leads to a development style I call "vibe coding" -- tweak a prompt, rerun, and if the output feels right, ship it.

This works for a demo. It is a recipe for disaster in production.

The Problem: Vibe Coding

Vibe coding: developing without a clear structure, relying on intuition and manual spot-checks.

Context

This is not a theoretical complaint. I see this pattern in every team adopting AI agents. The initial prototype is fast and impressive. Then it breaks in production because nobody wrote tests, nobody versioned the prompts, and nobody knows what the agent does with unexpected inputs.

Vibe Coding Symptom	What Goes Wrong
Monolithic code	Agent logic, prompts, and API calls tangled in one script
No tests	Verification means running the agent and eyeballing it
Fragile prompts	Treated as magic strings, no versioning or evaluation
Hidden risks	No boundaries, no tests for unexpected inputs or model changes

The result: systems that are brittle, impossible to maintain, and untrustworthy.

The Solution: Engineering-First Workflow

Project Structure

A clean project structure makes these principles easy to apply:

structured-agent-example/

structured-agent-example/
├── pyproject.toml      # Project definition and dependencies
├── README.md
├── src/
│   └── structured_agent_example/
│       ├── __init__.py
│       ├── agent.py        # Core agent logic
│       └── llm_service.py  # Mocked external service
└── tests/
└── test_agent.py     # Unit tests for the agent

A Sample Test

This test uses Python's unittest.mock to validate the agent's behavior without calling a real LLM:

tests/test_agent.py

@patch('structured_agent_example.llm_service.get_sentiment')
def test_run_positive_sentiment(self, mock_get_sentiment):
"""Tests the agent's run method with a mock."""
# Configure the mock to return a specific value
mock_get_sentiment.return_value = "positive"

agent = SentimentAgent({"model_name": "test-model-v1"})
text = "This is a great product, I love it!"
result = agent.run(text)

# Assert that our mock was called correctly
mock_get_sentiment.assert_called_once_with(text, model="test-model-v1")

# Assert that the agent processed the result correctly
self.assertEqual(result["status"], "success")
self.assertEqual(result["sentiment"], "positive")

This test verifies the agent's internal logic, not the LLM's accuracy.

Vibe Coding vs Engineering-First

Aspect	Vibe Coding	Engineering-First
Structure	Single script	Modular components
Testing	Manual spot-checks	Automated unit + integration tests
Dependencies	Direct API calls everywhere	Mocked, injectable services
Prompts	Hardcoded magic strings	Versioned, evaluated, configurable
Configuration	Scattered env vars	Config-as-code (YAML/.env)
Failure handling	Hope it works	Explicit error paths
Maintainability	Only the author understands it	Any engineer can contribute

Reality Check

"Structure is freedom" sounds like a platitude until you are debugging a production agent at 2 AM and realize the prompt changed three times, the mock was never updated, and the error handling path was never tested. The upfront investment in structure pays for itself on the first incident.

The four principles in detail

1. Modular Structure: Separate the code into distinct components -- the agent's main logic, services that interact with external APIs (like LLMs), and configuration. Each piece should be independently testable.

2. Test-Driven Development (TDD): Before writing the agent's core logic, write tests that define what it should do. This forces clarity about edge cases and desired outcomes before implementation.

3. Mocking Dependencies: Agent tests should never make real API calls. Mocking libraries simulate LLM behavior, keeping tests fast, predictable, and free.

4. Configuration as Code: Hardcoded model names, API keys, or prompts are a liability. Configuration files (YAML or .env) enable environment-specific behavior without code changes.

Why this matters for Drupal and WordPress

Drupal and WordPress agencies are increasingly building AI agents for content migration, SEO optimization, and automated site audits. The vibe-coding trap is especially dangerous here because CMS integrations touch live content databases. A monolithic agent that bulk-updates WordPress posts or Drupal nodes without proper mocking, test coverage, and error boundaries can corrupt production content. The modular structure and mock-first testing approach in this post directly applies to any agent that calls the WordPress REST API or Drupal's JSON:API.

What I Learned

• Structure is freedom. Good structure does not slow me down. It speeds me up by making the code easier to reason about and safer to change.
• Test the agent, not the AI. The goal of unit testing is to verify the agent's logic, error handling, and data transformations -- not to test the intelligence of the LLM.
• Start small. The principles of modularity and testing apply to even the simplest agent. My example project is under 50 lines of Python.
• The hard-won lessons of software engineering still apply to AI systems. "It works on my machine" is not a deployment strategy.

References

• View Code: structured-agent-example on GitHub

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Looking for an Architect who doesn't just write code, but builds the AI systems that multiply your team's output? View my enterprise CMS case studies at victorjimenezdev.github.io or connect with me on LinkedIn.