That Ansible playbook comparison is painfully accurate, haha. Your validator agent idea is smart, especially for catching A/B testing weirdness before it poisons the dataset.
The "lightweight local model" part is key, because the temptation is to just throw another GPT-4 call at the problem and call it a day. I've used a tiny fine-tuned BERT model for this exact sanity check on product attributes, and it ran on a small VM for pennies. Its only job was to go "does this scraped text *look* like a price?" with a simple yes/no, which filtered out button text like "Book a demo" or "Talk to sales."
But the catch is training data. You need a decent set of labeled "price-like" vs "not-price-like" text samples from your *actual* target sites to make that validator useful, otherwise it's just guessing. That's an extra step a lot of prototypes skip.
Data nerd out
Great example with the A/B testing. That's exactly the kind of edge case I'd be worried about in production.
I'm curious about your validator agent. What's the threshold for "lightweight"? Do you run it on every single data point, or just a sample? I'm trying to balance catching errors with keeping costs down, and I'm not sure where to draw that line.
Oh, the training data hurdle is the real killer, isn't it? You hit it perfectly. It's the classic prototype skip because building that dataset feels like actual work.
My workaround was stupidly simple: I let the first few expensive, unchecked runs *create* the training data. Every scrape result went into a log with a `needs_review` flag if it failed the basic regex. I'd manually review that log for a week (a boring hour of clicking), tag the good and bad entries, and suddenly I had a few hundred samples. Tossed that into a scikit-learn model, not even BERT, and got a validator that ran for literal fractions of a cent.
It's still a bootstrap process, but it turns that initial pain into a one-time cost that pays off when you're running the scraper daily. Without it, you're right - you're just guessing.
Thanks for sharing the code! It's really cool to see the planner and scraper set up so clearly. That clean coordination you mentioned is what got me excited about trying AutoGen myself.
Seeing the function map for Playwright in the scraper agent gives me a practical question. How are you handling timeouts or network errors within that fetch_page function? I'm wondering if you let the agents retry a few times, or if you have a separate error-handling step before they decide to move on to the next URL.
still learning
Excellent foundational setup. Regarding your question on handling timeouts and network errors within the `fetch_page` function, this is a critical operational detail often overlooked in initial prototypes. The strategy should be layered. The function itself should have built-in retry logic with exponential backoff and should differentiate between a transient network error (status 5xx, timeout) and a permanent client error (status 404, 403). I typically implement it like this:
```python
def fetch_page(url, retries=3, backoff_factor=2):
for i in range(retries):
try:
# ... Playwright navigation with explicit timeout
page.goto(url, wait_until="networkidle", timeout=30000)
break
except TimeoutError:
if i == retries - 1:
return {"url": url, "error": "timeout", "content": None}
sleep(backoff_factor ** i)
except Error as e:
# Handle other Playwright errors
return {"url": url, "error": str(e), "content": None}
```
However, the more interesting agent-coordination question is what happens after a failure is returned. Your planner agent should be configured to interpret the error object from the function map. A simple approach is to have the scraper agent, upon receiving a timeout or 5xx error, suggest a retry to the planner with a brief wait. For a 404, the planner should be instructed to log the failure and proceed to the next URL in its list. This moves error handling from a silent function fail into the agent conversation, making the workflow's decision logic transparent and auditable.
Yeah, that validator idea is smart until you realize you've just built a third system to babysit the other two. Ansible wasn't great, but at least when it broke, you knew why.
Your "N/A" column example is spot-on, but I've seen these local model validators add a new column: "validator_confidence: 0.87." Now you've got to decide what to do with the 13% it wasn't sure about. More human review.
Sometimes you just need a regex and a hard fail. Saves the weekend and the budget.
SQL is enough
You're not wrong. That confidence score just pushes the complexity downstream. I've watched teams spin up a whole dashboard just to monitor the spread of that uncertainty metric, which defeats the whole "lightweight" premise.
The regex hard fail is boring and reliable. It doesn't solve everything, but when it fails, the alert says "regex mismatch on price field" and you know exactly where to look. The "validator_confidence: 0.87" alert means you start your investigation by debugging the validator.