From: aidotengineer
Evaluating and optimizing AI agents requires considering practical aspects beyond their functional performance, particularly cost and latency [08:07:07], [08:14:00], [08:16:00].
Core Objectives
The primary goal in developing and optimizing AI agents is to ensure they achieve their objectives as quickly and cheaply as possible [08:27:00], [08:30:00], [08:32:00]. This involves:
- Cost and Latency Optimization [08:32:00]
- Number of Steps Optimization [08:36:00], [08:38:00]
The Double-Tier Optimization Challenge (Eval Ops)
When implementing evaluation methodologies for AI agents, a common pitfall is to adopt a “single-tier approach” [10:44:00]. This approach focuses solely on optimizing the “operative element flow,” meaning the agent itself [10:46:00], [10:48:00], [10:50:00], [10:52:00].
However, this often overlooks the costs, latencies, and uncertainties associated with the evaluation mechanism itself, referred to as “the charge” [11:00:00], [11:01:00], [11:04:00], [11:07:00].
To address this, it’s crucial to adopt a “Double tier” approach, optimizing both [11:21:00]:
- The operative LLM (Large Language Model) flow that powers the agent [11:23:00], [11:26:00].
- The chargement flow that powers the evaluations [11:28:00].
This complex situation is termed “Eval Ops” [11:31:00], [11:34:00], [11:37:00]. Evaluations can be complicated, expensive, and slow, warranting their own category of activities within this framework [11:43:00], [11:46:00], [11:49:00], [11:52:00]. Eval Ops is considered a special case of LLM Ops, operating on different entities and requiring distinct thinking, software implementations, and resource allocation to ensure accurate evaluations [12:08:00], [12:10:00], [12:13:00], [12:15:00], [12:19:00], [12:23:00], [12:24:00], [12:26:00], [12:29:00], [12:32:00].