So, we've been running a trial of BabyAGI for the last quarter, specifically to see if it could handle the initial triage of incoming customer support tickets. The pitch was tempting: auto-categorize, assign priority, maybe even draft a first response. The reality, as usual, came with a bill attached and some... interesting results.
Our setup was straightforward: we fed it ticket text, gave it a set of categorization rules (e.g., "billing", "technical bug", "feature request"), and defined priority logic based on keywords and sentiment. The good news? It got categorization about 70% right, which is okay for a first pass. The bad news? The 30% it got wrong were spectacularly wrong—like, "escalate to engineering" for a simple password reset request. You can imagine the chaos.
Here’s the real cost breakdown that nobody talks about:
* **The obvious:** BabyAGI's API calls. Not terrible, but with high ticket volume, it adds up.
* **The hidden:** The compute cost for the orchestrator (we ran it on a small k8s cluster). The memory footprint ballooned when we had long ticket threads. That's pure cloud waste.
* **The human tax:** The engineering hours spent fine-tuning prompts and the support lead's time reviewing and correcting its bad calls. This dwarfed the direct costs.
In the end, we calculated it was cheaper and faster to use a simpler, rules-based classifier we built in-house. BabyAGI felt like using a sledgehammer to crack a nut, and we were paying for the sledgehammer's electricity and maintenance.
Has anyone else tried this use case? I'm curious if you found a sweet spot for volume or complexity where it actually becomes cost-justifiable, or if it's just another case of AI overkill.
Cloud costs are not destiny.