From: aidotengineer
Evaluating AI systems, particularly those that provide recommendations, presents unique challenges, especially within complex multi-agent architectures [00:19:09]. Determining the quality of a recommendation in a system with many interconnected engines and rounds of conversation requires robust mechanisms [00:19:13].
Evaluation Challenges and Approaches
The primary challenge lies in understanding how to definitively assess if a recommendation is good [00:19:03]. To address this, a closed-loop system incorporating human scoring, structured feedback, and iterative revision cycles is crucial [00:19:26].
Human Evaluation
At early stages of development, human evaluation is considered the most effective method for assessing AI recommendations [00:19:34], [00:19:38]. While LLM-based evaluations are useful, they often do not provide the specific insights needed to drive necessary improvements [00:19:42], [00:19:46], [00:19:50].
Internal Evaluation Tool: Eagle Eye
An internal human evaluation tool, named “Eagle Eye,” was developed to facilitate this process [00:19:55], [00:19:59]. This tool allows for detailed inspection of specific cases within the AI system, including:
- The architecture being evaluated [00:20:06].
- Extracted requirements [00:20:09].
- Conversations between agents [00:20:11].
- The final generated recommendations [00:20:12], [00:20:16].
Through “Eagle Eye,” evaluators can conduct studies on relevance, visibility, and clarity, assigning scores to inform decisions on areas needing improvement [00:20:22], [00:20:26], [00:20:29], [00:20:32].
Key Learnings on AI Evaluation
- Confidence is Not Correctness: An AI system’s confidence level does not directly equate to the correctness of its output and cannot always be trusted [00:20:38], [00:20:41], [00:20:47].
- Early Human Feedback is Essential: Human feedback is critical early in the development of AI systems built from scratch [00:20:51], [00:20:52], [00:20:54], [00:20:56].
- Evaluation Must Be Integrated into Design: Evaluation should be a foundational component of system design, not an afterthought [00:20:59], [00:21:00], [00:21:03]. When designing a new AI system, parallel consideration should be given to how it will be evaluated, whether through human review, monitoring dashboards, or LLM-based feedback loops [00:21:10], [00:21:13], [00:21:17], [00:21:19], [00:21:21], [00:21:24], [00:21:28], [00:21:31], [00:21:34].
Identifying Issues: Hallucination Example
The evaluation tool helps identify issues like hallucination. For instance, an early case involved a “staff architect network security” agent requesting a workshop schedule from a “requirements retriever” agent, providing specific dates, which was an instance of the agent hallucinating an action outside its defined capabilities [00:22:05], [00:22:07], [00:22:10], [00:22:13], [00:22:17], [00:22:21], [00:22:24], [00:22:26]. Handling such cases is crucial for system improvement [00:22:31].
Effective evaluation is an ongoing process of experimentation, learning which patterns work best with the available data, and refining agent interactions and autonomy [00:24:16], [00:24:18], [00:24:21], [00:24:23], [00:24:25], [00:24:28], [00:24:39], [00:24:42], [00:24:46], [00:24:48].