My results after using AI-generated code without any human review for a week (bad idea)

I've long advocated for the principle that all code, regardless of origin, must pass through the same rigorous review and testing gates before reaching production. This includes AI-generated code. Last week, I decided to conduct an unsanctioned, personal experiment: for all non-critical scripting and tooling tasks, I would accept the first, unedited output from a coding assistant and deploy it directly, bypassing my own review. The goal was to quantify the hidden toil this "shortcut" introduces. The results were predictably disastrous and served as a potent object lesson.

The tasks ranged from simple data parsing scripts to a configuration generator for a new monitoring check. Here is a representative example of the raw output for a Python script intended to parse application logs and extract error rates, which I then ran directly.

```python
import os
import re

def parse_logs(log_directory):
error_count = 0
total_lines = 0
for filename in os.listdir(log_directory):
if filename.endswith('.log'):
with open(os.path.join(log_directory, filename), 'r') as f:
lines = f.readlines()
for line in lines:
total_lines += 1
if re.search(r'ERROR|FAILED', line):
error_count += 1
if total_lines > 0:
error_rate = (error_count / total_lines) * 100
else:
error_rate = 0
return error_rate

print(f"Error rate: {parse_logs('/var/log/myapp')}%")
```

Superficially, it works. Practically, it's a reliability nightmare. The issues I encountered across multiple such scripts fell into distinct, critical categories:

* **Silent Failures and Lack of Resilience:** The code above has no error handling for missing directories, permission issues, or unreadable files. It would crash and provide zero output, a cardinal sin for automated monitoring scripts.
* **Resource Inefficiency:** Reading entire multi-gigabyte log files into memory (`f.readlines()`) is a non-starter. It would cause the monitoring agent itself to OOM kill.
* **Logical & Security Flaws:** In another script for generating a cloud config, the assistant used hard-coded placeholder credentials in a variable named `SECRET_KEY`. It also consistently missed edge cases, such as off-by-one errors in loop conditions.
* **Ignorance of Existing Conventions:** Generated scripts did not follow our team's established patterns for logging, metric emission, or configuration management, making them alien and unmaintainable in our ecosystem.

The operational impact wasn't theoretical. Within the week, I experienced:
- A data aggregation script that silently failed due to a `FileNotFound` exception, leading to stale dashboards.
- A deployment script that did not implement proper idempotency or rollback, leaving a test environment in a fractured state requiring manual intervention.
- Cumulative hours spent in reactive debugging sessions, tracing problems not to my requirements, but to the *implementation choices* of the AI model.

The conclusion is stark. AI-generated code, without human review, is akin to accepting a complex subsystem from an inexperienced intern who has never participated in an on-call rotation. It lacks the tacit knowledge of production pressures, failure modes, and organizational context. The velocity gain is illusory, as it directly trades off with future reliability and increases systemic toil. My workflow recipe is now simple: the coding assistant is a powerful **drafting tool**, but its output must be subjected to the same SRE review checklist as any other code.

* Does it handle all expected failure modes?
* Is it resource-efficient for the data scale?
* Does it adhere to internal patterns and conventions?
* Are secrets and configuration appropriately externalized?
* Are there clear, actionable log outputs for operational debugging?

The code only graduates from draft status once these questions are satisfied.

monitor first