From: aidotengineer
Evaluation and feedback are critical components when developing complex AI systems, especially those with multiple moving parts, like multi-agent architectures [03:42]. For Cat.io’s AI copilot for cloud architecture, addressing the challenge of how to evaluate and provide feedback to their large AI system was paramount [03:42].
Challenges in AI Evaluation
A significant challenge in AI agent evaluation is determining if a recommendation generated by the system is truly good [19:03]. For a multi-agent system with numerous engines and rounds of conversations, monitoring what works best requires careful consideration [19:13].
Best Practices for AI Evaluation
To effectively evaluate AI systems and facilitate continuous improvement in AI systems, Cat.io found it essential to:
- Close the loop with human scoring and structured feedback, incorporating revision cycles [19:26].
- Prioritize human evaluation, especially in early stages [19:34]. While LLM evaluations are useful, they often don’t provide the specific insights needed for targeted improvements [19:42].
- Bake evaluation into the system design from the very beginning, rather than adding it as an afterthought [20:56]. This proactive approach ensures that evaluation mechanisms are considered as soon as a new AI system is designed [21:10].
Implementation of Evaluation Platforms for AI Agents
Cat.io developed an internal human evaluation tool called “Eagle Eye” to facilitate the implementation of evaluation platforms for AI agents [19:55]. This tool allows evaluators to [19:59]:
- Examine specific cases, including the architecture and extracted requirements.
- Review conversations between agents.
- Assess generated recommendations.
Evaluators use “Eagle Eye” to perform relevance, visibility, and clarity studies, assigning scores that guide future development priorities [20:22]. The tool provides a detailed view of interactions within the multi-agent system, allowing users to read conversations and determine if they make sense [21:43].
Key Learnings in Evaluation
Through their development process, Cat.io identified several crucial lessons regarding improving AI evaluation methods:
- Confidence is not correctness: An AI system’s confidence in its output does not always equate to accuracy or correctness [20:38].
- Human feedback is essential early on: When building AI systems from scratch, human feedback is vital for initial development and refinement [20:52]. This underscores the role of evaluators in AI development.
- Evaluation must be integrated: Evaluation tools, whether human-driven, monitoring dashboards, or LLM-based feedback loops, should be an integral part of the system’s design from the outset [20:56].
Hallucination Detection
The “Eagle Eye” tool helped identify early cases of hallucination, such as a staff architect (network security) attempting to schedule a workshop with specific dates when interacting with a requirements retriever [22:05]. Detecting and handling such instances is critical for system reliability [22:31].