In this engaging session at TestMu Conference 2025, ššš¦šš¬ ššš¬š¬š, Senior Executive Director at JPMorganChase, explores the intricacies of testing Large Language Model (LLM) agents. James shares his expertise in AI data testing and cloud transformation, offering strategies for ensuring the reliability and trustworthiness of these agents.
The session covers key challenges in hallucination detection, data quality, and agent orchestration, with a focus on building agentic trust within AI systems. James also dives into practical techniques for preventing faulty outputs and ensuring agents perform consistently across various tasks.
Building trust in LLM agents: Key strategies to enhance agent reliability.
Hallucination detection: Methods to identify and address false outputs.
Agent orchestration: Managing multiple agents to work together seamlessly.
Evaluating agent performance: Key metrics for assessing accuracy and relevance.

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