From: aidotengineer
Evaluating AI agents and assistance is crucial to ensure they function effectively in real-world production environments [00:00:55]. While the development of various AI agents and tools receives significant attention, understanding their performance post-deployment is equally vital [00:00:45]. This article delves into the methods and metrics for assessing AI agents, with a specific focus on the challenges in AI agent evaluation, particularly regarding path convergence.
Components of an AI Agent
Regardless of the framework used (e.g., Lan graph, Crei, Llama Index Workflow) [00:03:25], AI agents typically consist of common architectural patterns [00:03:38]:
- Router: This component acts as the “boss” [00:03:52], deciding the agent’s next step [00:03:07]. For instance, in an e-commerce agent, a router determines whether a user query like “I want to make a return” should trigger a customer service skill, a discount suggestion, or a product search [00:04:06].
- Skills: These are the logical chains that perform the actual work [00:03:12]. A skill flow might involve LLM calls or API calls to execute a user’s request [00:05:02].
- Memory: This component stores past interactions to enable multi-turn conversations, preventing the agent from forgetting previous statements [00:05:24].
Evaluating AI Agent Performance
Each component of an AI agent presents an opportunity for errors [00:07:53]. Therefore, evaluating AI agents and assistance requires assessing each part. Engineers building and troubleshooting agents typically examine “traces” to understand the internal workings [00:06:07].
Evaluating the Router
For routers, the primary concern is whether the correct skill was called [00:07:58]. If a user asks for leggings but is sent to customer service, the router made an incorrect decision [00:08:07]. It’s also important to verify that the router passes the correct parameters to the chosen skill, such as material type or cost range for a product search [00:08:47].
Evaluating Skills
Skill evaluation involves assessing multiple aspects, especially in Retrieval-Augmented Generation (RAG) type skills [00:09:43]. This includes:
- Relevance: Checking the relevance of retrieved chunks [00:09:47].
- Correctness: Ensuring the generated answer is correct [00:09:52]. Evaluations can involve LLM-as-a-judge assessments or code-based evaluations [00:10:00].
Evaluating Agent Path and Convergence
One of the most challenging aspects of evaluating AI agents methods and metrics is assessing the agent’s path or “convergence” [00:10:14]. Ideally, when a skill is called multiple times for the same task, it should consistently take a similar, succinct number of steps to complete the query, input parameters, call components, and generate the correct answer [00:10:20].
The challenge in creating effective AI agents arises because the number of steps an agent takes can vary wildly, even when built with different LLMs like OpenAI versus Anthropic [00:10:56]. The goal is to ensure:
- Succinctness: The agent takes the most efficient path [00:11:04].
- Reliability: The number of steps remains consistent [00:11:05].
- Consistency: The agent reliably completes tasks [00:11:07].
Measuring and evaluating this “convergence” or counting the number of steps is considered one of the hardest challenges in AI agent development [00:11:16].
Special Considerations for Voice AI
Voice AI, which is revolutionizing call centers with over a billion calls made globally [00:01:52], introduces additional layers of evaluation complexity for multimodal agents [00:02:29]. Beyond text or transcript evaluation, the actual audio chunks must be assessed [00:12:10]. This includes evaluating:
- User sentiment [00:12:30]
- Speech-to-text transcription accuracy [00:12:34]
- Consistency of tone throughout the conversation [00:12:36]
Multi-Layered Evaluation for Debugging
Effective strategies for AI evaluation and troubleshooting involve implementing evaluations at every step of an agent’s trace [00:14:40]. This allows for precise debugging when issues arise [00:14:52]. For example, if an agent’s response is incorrect, evaluations across the entire application’s flow — at the router level, skill level, and throughout the execution — help pinpoint exactly where the error occurred [00:14:55].
Example
The speaker’s company evaluates their own co-pilot by running evaluations at each step of its trace [00:13:51]. This includes:
- Overall correctness of the generated response [00:14:05].
- Whether the search router picked the correct router and passed the right arguments [00:14:21].
- If the task or skill was correctly completed in its execution [00:14:32]. This multi-layered approach highlights the technical challenges in AI agent development and the importance of granular evaluation for effective debugging.