Optimizing AI Agents for Targeted Applications

Aniket Abhay Kulkarni (~aniket-ak)




AI agents, leveraging large language models (LLMs), are pivotal in transforming how digital systems operate by executing complex, real-world tasks beyond mere query processing. Despite their advancements, existing agents often face challenges such as looping behaviors or sensitivity to slight input variations, which can impede their practical application and effectiveness.

Our approach at Newtuple Technologies involves developing domain-specific agents that excel in designated functions, employing a modular architecture that enhances precision and efficiency. The key design principles include selecting the right LLM for specific tasks and using a structured data exchange format like JSON, which allows for scalability and adaptability. This specialized focus ensures that agents perform with high accuracy and can be easily customized or scaled according to need. Rigorous benchmarking and continuous evaluation of each component are integral to optimizing agent performance, ensuring they remain robust in dynamic settings.

In conclusion, by adhering to strategic design principles and focusing on targeted applications, AI agents can achieve near-human accuracy, handling tasks with remarkable efficiency and cost-effectiveness. Our experiences demonstrate that well-designed AI agents can significantly reduce operational costs, accomplishing 80-90% of what humans can at a fraction of the time and expense, thereby delivering substantial value in professional settings.


Working knowledge Large Language Models (LLMs) is desired. Preliminary knowledge on AI agents is bonus.

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Speaker Info:

I work at Newtuple Technologies as Head of AI. I come from Machine Learning and Data Science background with 10 years of industry experience.

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Section: Artificial Intelligence and Machine Learning
Type: Talk
Target Audience: Advanced
Last Updated: