Architecting Agentic AI — Beyond Correctness: Evaluating Conversations, Trust & Outcomes with Kiro…
📰 Medium · AI
Learn to evaluate Agentic AI beyond just correctness, focusing on conversation, trust, and outcomes
Action Steps
- Evaluate AI agent conversations using metrics like engagement and coherence
- Assess trust in AI agents through user feedback and behavioral analysis
- Analyze outcomes of AI agent interactions to inform future improvements
- Develop a framework for evaluating AI agents that goes beyond just correctness
- Implement Kiro or similar tools to support evaluation and improvement of Agentic AI
Who Needs to Know This
AI engineers and researchers benefit from understanding how to assess AI agents' behavior, enabling them to develop more effective and trustworthy AI systems
Key Insight
💡 Evaluating AI agents requires a holistic approach that considers conversation, trust, and outcomes, not just correctness
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🤖 Evaluating Agentic AI? Go beyond correctness! Assess conversations, trust, and outcomes to build more effective AI systems
Key Takeaways
Learn to evaluate Agentic AI beyond just correctness, focusing on conversation, trust, and outcomes
Full Article
Evaluating an AI agent isn’t just about verifying the final response — it’s about understanding how the agent behaves throughout the… Continue reading on Medium »
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