Just wrapped up a 3-week pilot using BabyAGI to manage inventory alerts for our 200-store retail k3s cluster. Wanted something lightweight that could parse sales data and trigger restock workflows.
The core agent was surprisingly simple. Had it watch a PostgreSQL DB (sales data) and post to a webhook. Here's the basic task list I started with:
```yaml
objective: "Monitor hourly sales vs. stock levels, flag items needing restock."
task1: "Query DB for items where stock < (sales_last_6h * 2)."
task2: "Format flagged items into JSON with store_id, item_sku, urgency."
task3: "POST JSON to internal inventory API webhook."
```
Ran into the classic context window crunch fast. Had to chunk the query results and summarize before the final task. Also, the built-in loops got expensive quick. Switched to a cron-driven execution every 6 hours instead of continuous.
For a fixed, well-scoped workflow like this, it worked. But I'm curious—anyone else try BabyAGI for internal data agents? Did you move to a more deterministic pipeline (like Argo Workflows) or stick with the agent pattern? The autonomous task creation felt overkill here.
yaml all the things
So you ran into the "classic context window crunch." Not surprising, given the agent was trying to parse raw DB rows. Did you at least get an itemized runbook from that cost spike, or did it just vanish into the monthly cloud bill?
You're right that autonomous task creation is overkill for a deterministic workflow. But moving to a cron job just papers over the core issue: you're still paying for a full LLM chain to run what's essentially a SQL query and a curl command. Why not just write a 50-line Python script and save yourself the compliance headache of having an "AI agent" touching your inventory data?
- Nina
> "Why not just write a 50-line Python script"
You're totally right for the core monitoring logic. That's exactly what I ended up doing for the hourly check. The Python script handles the query and the POST.
But I kept a tiny BabyAGI wrapper for one specific job: writing the weekly summary report. The script dumps last week's flagged items and resolution times into a CSV, and the agent drafts a narrative analysis from it. Saves me 20 minutes every Monday.
It's a good hybrid approach. Use code for the predictable, repetitive work, and only call the LLM where you actually need language understanding.