About this session
We’re all excited to build and deliver agentic AI services. But what about running at the exponentially greater scale that agents create? LLMs suffer from poor latency and availability issues. More frequent model training drives more frequent updates to agentic services. Most of all, the LLM cost of running at agentic scale breaks the bank—fast.
So, what can you do?
In this session, we dug into how engineering and operations can address:
- Making agentic services fail-proof when their LLMs were not
- Managing a two-order-of-magnitude increase in TPS, including a 2M TPS RAG case study
- Navigating cost vs. quality tradeoffs, with LLMs costing up to 100,000x more than a database transaction
- Continuously redeploying agents that require frequent retraining
Click here to view the presentation slides.
