Break It ‘Til You Make It Building the Self-Improving Stack for AI Agents - Aparna Dhinakaran
AI Summary
In this talk, Aparna Dhinakaran discusses the essential processes involved in maintaining the effectiveness of AI agents after their deployment. The presentation focuses on the importance of creating a comprehensive monitoring and improvement stack that ensures agents remain reliable and efficient while adapting to challenges they encounter over time. Key topics include the development of evaluation layers to expose failure modes, the role of tracing and instrumentation for enhanced visibility into agent performance, conducting experiments to improve outcomes, and the significance of feedback-driven optimization. Dhinakaran emphasizes the need for continuous improvement not just in agent performance but also in the evaluation processes, making this talk valuable for those looking to scale AI agents from prototype to production.
Watch the full video here: Break It ‘Til You Make It