A practical workshop on implementing evaluation-driven testing for LLMs and AI agents and chatbots 

Your Pass/Fail Tests Are Lying to You About AI Quality

Your AI agent passed all your tests. Then it confidently told a customer your return policy is 90 days (it’s 30). 

The problem? You tested it like software. AI needs evaluation, not validation.

This isn’t a theory session. This is a hands-on session where you’ll learn how Pcloudy’s   Agent Evaluation Platform helps you build continuous evaluation frameworks that catch hallucinations, measure response quality, and monitor production AI.

What You’ll Learn in This Session: 

By the end of this workshop, you’ll know how to use Pcloudy’s platform to: 

✓ Set up automated evaluation pipelines that score AI responses for accuracy, hallucinations, and consistency 
✓ Implement agent-to-agent testing where Pcloudy’s AI agents test your AI agents at scale 
✓ Build quality benchmarks and deployment gates (accuracy thresholds, consistency requirements) 
✓ Monitor production AI with continuous evaluation and real-time alerts 
✓ Scale testing from 10 to 10,000 scenarios using synthetic test generation 

This is evaluation-driven development—see how Pcloudy makes it practical for QA teams without ML expertise.