Semantic and behavioral evaluation of agents

From: aidotengineer

Agent evaluation is considered both an art and a science, yet it is essential to ensure that AI agents perform as expected, particularly when deployed in production environments [00:00:12].

Core Divisions of Agent Evaluation

Agent evaluation can be clearly divided into two main parts: semantic and behavioral [00:00:44].

Semantic Evaluation

The semantic part focuses on how the agent’s internal representations of reality relate to actual reality [00:00:50]. Truthfulness in semantic evaluation is achieved by grounding these representations in data, often through Retrieval Augmented Generation (RAG) [00:02:06].

Single-Turn Semantic Quality

This aspect covers “single step” or single turn items, which are non-agenic universal virtues [00:01:29] [00:03:22]. These include:

• Consistency: Whether the agent’s reply to the user is consistent [00:03:37].
• Safety: Ensuring the content is safe [00:03:40].
• Alignment: Verifying that the agent’s statements align with the values and policies of stakeholders or organizations [00:03:48].

Retrieval Augmented Generation (RAG) or attention management requires specific evaluators to measure aspects such as:

• Correctness of the retrieved context [00:04:15].
• Comprehensive recall of information [00:04:20].
• Faithfulness, which relates answers to external reality [00:04:24].
• Answer-question relevance and factfulness, which relates to reality beyond just the reference data [00:04:33].

Multi-Turn Semantic Quality

In the multi-turn context, semantic evaluation involves examining chat conversation histories [00:05:24]. This includes looking for consistency and adherence to topics, while also recognizing when topic changes are appropriate [00:05:33]. Another crucial aspect is the evaluation of reasoning traces, such as a Chain of Thought, which represents sequential activities and representations of the world before any actions are taken [00:06:00].

Behavioral Evaluation

The behavioral part focuses on how the agent’s actions and the tools it uses contribute to achieving its goals within its environment and their ultimate effects on that environment [00:01:05]. Goal achievement in behavioral evaluation is accomplished by grounding the agent’s activities in the tools it has available [00:02:21].

Individual Tool Selection and Usage

Before considering chains of behaviors, individual tool usage must be evaluated [00:06:58]. Key evaluation points include:

• Whether the agent follows instructions [00:06:33].
• Correct extraction of tool characteristics [00:06:36].
• Correct selection of the right tool [00:06:41].
• Quality of the tool’s output [00:06:44].
• Correct handling of error situations [00:06:47].
• Adherence to structured tool formats [00:06:50].

Task Progression and Planning (Multi-Step Behaviors)

When evaluating a chain of behaviors or multi-step/multi-turn cases, the focus shifts to whether the agent’s actions are converging towards achieving its goal [00:07:04]. This also includes assessing the consistency and quality of the agent’s plan [00:07:22].

Grounding in External Reality

Both semantic representations and behavioral activities are ultimately grounded in external reality [00:07:33]. Representations are grounded on truthfulness, while activities and behaviors are grounded by goal achievement and utility [00:07:40]. Goal achievement and utility serve as the ultimate metrics, with other evaluation points acting as proxy metrics [00:07:53].

Other Practical Considerations for Agent Evaluation

Beyond the semantic and behavioral map, several practical considerations are vital for evaluating AI agent performance and reliability:

• Cost and Latency Optimization: Agents should progress towards their goals as quickly and cheaply as possible [00:08:24]. This includes optimizing the number of steps an agent takes [00:08:36].
• Tracing and Debugging: The ability to identify where an agent went wrong is crucial [00:08:42].
• Error Management: This specifically refers to dealing with errors related to tool usage, distinct from semantic errors in the agent’s inference process [00:08:49].
• Offline vs. Online Testing: A key distinction is between evaluations conducted during development (offline) and those performed during the agent’s live online activities [00:09:06].
• Special Cases and Tool-Specific Metrics: Depending on the agent’s function, more refined and advanced metrics may be needed [00:09:41]. For example, tool-specific metrics, such as those for API calls, can be measured using traditional software testing methodologies [00:09:59].

LLM as a Judge and Eval Ops

Many measurements in agent evaluation are implemented using Large Language Models (LLMs) as “judges” or “chargers” [00:10:26]. However, a common pitfall is adopting a “single-tier approach,” where optimization focuses solely on the agent’s operational element flow [00:10:44].

It is crucial to recognize the “double-tier” requirement: optimizing both the operative LLM flow that powers the agent and the “charger” LLM flow that powers the evaluations [00:11:19]. This complex situation is referred to as “Eval Ops” [00:11:34]. Eval Ops is a specialized category of LLM Ops, dealing with unique entities, requiring different thinking, software implementations, and resource allocation to ensure accurate and effective evaluations [00:12:08].