u/RepublicMotor905

we're running an agentic pipeline that does multi-modal file processing - large files, often hundreds of mb per request. The actual agent logic works fine. but the infrastructure is not.

during peaks the queue backs up fast. But staying provisioned at peak capacity 24/7 would eat our runway during the slow periods. Standard cpu/memory-based autoscaling is the wrong signal here - gpu utilization under inference workloads doesn't behave the way normal compute does. you can have a node that looks underutilized on conventional metrics while your queue is actually backing up.

how others have handled this?

our ai agent worked fine in the pilot, but now that it's chewing on real production data, things are falling apart fast.

the main problem is compounding errors. it makes one slightly off tool call, and by step four it's hallucinating a solution or stuck in a loop. also caught it trying to reach for tools it shouldn't even have access to for the task it's running.

what are you building around the model to keep it stable? feel like i'm missing some basic engineering principle here and just throwing prompts at the problem.

scaling up our use of autonomous agents and at what point does a company actually need a dedicated AI-SPM layer, versus when is it just adding complexity?

the way I think about it: AI-SPM is the control layer that shows you what your agents can actually touch, not just what your access policies say they should. traditional CSPM tells me the server configuration looks fine. it doesn't tell me if an agent is one prompt away from exfiltrating customer PII through an over-permissioned retrieval pipeline.

is this on your 2026 roadmap, or are you still working through basic LLM governance first?

seeing a lot of cool prototypes around here lately, but what everyone's stack actually looks like when you have to take something live. 3,000+ complex transactions a month, real error handling, agents that don't randomly go off the rails.

we just wrapped a 3-month build for a high-volume hiring platform, but looking for different experience. what does your boring-but-reliable stack look like for 2026?