Moved from a single complex agent to a CrewAI setup. Regret. More points of failure.

I finally bit the bullet and refactored a key data quality monitoring pipeline from a single, admittedly large, Python agent script into a CrewAI setup. The idea was sound: separate concerns, make it more maintainable, maybe even scale it. Two weeks in, and I'm already planning the rollback. It's a classic case of over-engineering.

My old script was a monolith, but it was a *predictable* monolith. It did three things in sequence: fetch validation rules from the warehouse, run checks on the data, and then post failures to our alert channel. Now, I have three agents (`RuleFetcherAgent`, `DataValidatorAgent`, `AlertCoordinatorAgent`) with their own prompts, a crew orchestrating them, and a `Task` for each step. The boilerplate tripled. What used to be a simple script I could run with `python monitor.py` is now a small application with hidden state.

The new failure modes are maddening.
* The handoff between agents is brittle. If `RuleFetcherAgent` returns a JSON that `DataValidatorAgent`'s LLM call decides to format slightly differently, everything breaks. You're now debugging parsing logic *between* LLM calls.
* Cost and latency went up for no gain. Instead of one LLM call that handled the logic, I now have three sequential calls, each with their own context and overhead.
* The "orchestration" is now the problem. Did the crew fail, or did an agent fail? Is a task stuck? With the old script, stack traces were linear and clear.

Here's a simplified snippet of the crew setup I'm dealing with. Look at all this scaffolding for a linear three-step process.

```python
# This is now just the *definition*, not even the execution
rule_fetcher = Agent(
role="Rule Fetcher",
goal="Fetch data validation rules from Snowflake",
backstory="...",
verbose=True,
llm=llm
)

validation_task = Task(
description="Fetch rules from {table} and validate data for {date}",
agent=rule_fetcher,
expected_output="A JSON list of failed validation rules."
)

crew = Crew(
agents=[rule_fetcher, validator, alerter],
tasks=[fetch_task, validation_task, alert_task],
verbose=2
)

result = crew.kickoff() # And now you pray.
```

The promise was better modularity, but I've just traded a known devil for an unknown one. The single agent was a known quantity—I could add conditional logic and error handling in plain Python. Now, the control flow is buried in CrewAI's execution loop.

Has anyone else found a sweet spot for CrewAI? Maybe for truly parallel, independent tasks? For linear ETL/logic, this feels like fitting a race car engine into a go-kart. You get more moving parts, more noise, and a slower, less reliable ride. I'm considering pulling the core logic back into functions and using CrewAI only if I ever need *true* parallel agent execution, which for most data pipelines, is almost never.