From: aidotengineer
Agent evaluation is considered both an art and a science, and is crucial for ensuring that AI agents perform as expected, especially when deployed in production [00:00:12]. The evaluation process can be broadly divided into semantic and behavioral components [00:00:44].
Semantic Evaluation Defined
The semantic part of agent evaluation focuses on how an agent’s internal representations of reality accurately relate to external reality [00:00:50]. Truthfulness in these representations is achieved by grounding them in data [00:02:06].
Semantic evaluation is further categorized into single-turn (or single-step) and multi-turn aspects [00:01:26].
Single-Turn Semantic Quality
Single-turn semantic quality involves assessing individual responses or outputs from the agent. This includes:
- Universal Virtues [00:03:13]: These are general qualities, often non-agenic, but essential for a complete evaluation [00:03:22]. Examples include:
- Coherence and Consistency: Is the agent’s reply consistent? [00:01:33] [00:03:37]
- Safety: Is the content safe? [00:03:40]
- Alignment: Does the agent’s output align with the values of stakeholders and adhere to organizational policies? [00:03:48]
- Retrieval-Augmented Generation (RAG) and Attention Management: This requires specific evaluators to measure aspects such as:
- Whether the retrieved context was correct [00:04:15].
- Whether all relevant information was comprehensively recalled [00:04:20].
- Faithfulness: How well the answers relate to external reality, which is distinct from answer/question relevance or general factfulness [00:04:22] [00:04:27]. RAG evaluations involve examining relationships between different parts of the RAG pipeline [00:05:04].
Multi-Turn Semantic Quality
Multi-turn semantic quality extends evaluation to sequences of interactions and the agent’s reasoning processes:
- Chat Conversation Histories: Assessing how chat conversation histories develop [00:05:27], including:
- Overall consistency and adherence [00:05:33].
- Sticking to topics when necessary, or allowing topic changes if desired by the user [00:05:37].
- Reasoning Traces: Evaluating the agent’s internal reasoning processes, such as the Chain of Thought [00:06:06]. This provides a way to assess sequential or multi-turn activities performed by the agent in its reasoning and world representation before taking any actions [00:06:15].
Practical Considerations
Many of these semantic measurements are implemented using LLMs as judges [00:10:28]. A common pitfall is focusing solely on optimizing the operative element flow (the agent itself), neglecting the cost, latency, and uncertainty of the judging LLM (the “chargement” flow) [00:10:44]. It is crucial to adopt a “Double Tier” approach, optimizing both the operative LLM flow powering the agent and the chargement flow powering the evaluations [00:11:10] [00:11:21]. This complex situation is referred to as “Eval Ops,” which is a specialized form of LLM Ops, requiring different thinking, software implementations, and resourcing [00:11:34] [00:12:08].