Aditya Palnitkar will be speaking at ODSC East from May 13th to 15th, focusing on the critical topic of evaluating AI agents in his talk, “Evals for Supercharging your AI Agents.” Effective evaluations are often neglected in LLM application development, yet they play a vital role in ensuring quality and user satisfaction. Aditya emphasizes two key steps: creating custom metrics tailored to human-AI interactions and building robust datasets for evaluation. Attendees can gain insights into generating high-quality evaluation datasets, using AI for simulation tests, and setting performance metrics aligned with business goals. Join him to explore how a strong evaluation system can enhance your AI agent development process.
Aditya Palnitkar to Unveil AI Agent Evaluation Strategies at ODSC East 2025
Editor’s note: Aditya Palnitkar will be presenting at the upcoming ODSC East conference from May 13th to 15th. Attend his talk titled “Evals for Supercharging Your AI Agents” to gain valuable insights into effective AI agent evaluations.
Evaluating AI agents and LLM applications is crucial yet often neglected during development. Implementing a robust evaluation system can significantly enhance your development process. Here are some key benefits of a good evaluation framework:
– Catch regressions to ensure you adhere to the “do no harm” principle.
– Set goals with metrics that align with user experience.
– Build a roadmap that identifies areas needing improvement.
Aditya’s blog provides practical strategies to tackle these challenges, including using scalable LLM judges and conversation-level metrics to gain deeper insights.
So, how do you create a solid evaluation system? It involves two main steps:
Step 1: Build Your Own Metric (BYOM)
Unlike traditional machine learning tasks, AI agents that interact with humans require custom metrics. For instance, an AI realtor shouldn’t just provide accurate information but also must avoid biased responses. Similarly, a medical AI must work to eliminate inaccuracies while prioritizing patient safety.
Step 2: Build Your Own Dataset (BYOD)
After defining your metrics, it’s time to focus on creating your dataset. Establish a labeling pipeline that effectively assesses the quality of your AI agent’s responses. This process may involve partnering with experts to ensure accurate labeling and setting up a human labeling system to maintain high standards.
Although establishing an evaluation pipeline involves significant effort, it creates powerful feedback loops that can help refine AI agent development. Aditya’s upcoming session at ODSC East will detail how to create an effective AI agent evaluation system, including how to generate high-quality datasets and monitor potential failures.
For those keen on harnessing the potential of AI agents, attending his session is a must.
Author Bio
Aditya Palnitkar is a staff engineer at Meta, with extensive experience in AI and machine learning. He holds a master’s degree in Computer Science from Stanford and has contributed to state-of-the-art recommendation models at Facebook. Currently, he is leading a team focused on evaluating AI agents at Meta.
Tags: AI Agents, LLM Applications, Evaluation Strategies, ODSC East 2025, Machine Learning, Aditya Palnitkar
What is “Supercharge Your AI Agents with Evaluations”?
“Supercharge Your AI Agents with Evaluations” is a guide to improving your AI systems. It focuses on ways to assess and enhance the performance of AI agents through evaluations. This helps make AI more effective in tasks.
Why do I need to evaluate my AI agents?
Evaluating your AI agents is important because it shows how well they perform. Regular evaluations can highlight areas for improvement and ensure that your AI gives the best possible results in various tasks.
How do I start evaluating my AI agents?
To start evaluating your AI agents, first define clear goals. Decide what aspects you want to measure, such as accuracy or speed. Then, create suitable tests and analyze the results to pinpoint strengths and weaknesses.
What tools can I use for evaluation?
There are several tools available for evaluating AI agents. Some common options include performance metrics software, analytics platforms, and machine learning frameworks. Choose tools that fit your specific needs and objectives.
Can evaluations help in long-term improvements?
Yes, evaluations can lead to long-term improvements. By regularly assessing your AI agents, you can track progress over time and make informed adjustments. This continuous process helps ensure your AI remains efficient and effective as it evolves.
