Building and maintaining an AI agent in production can be challenging, especially when working with large language models (LLMs). This blog shares the lessons learned from developing and improving their AI Assistant, Copilot, using tools like Arize and Phoenix. It explains how they test new features, monitor user interactions, and continuously iterate to enhance performance. Tracking metrics through dashboards helps identify trends and potential issues. The team emphasizes the importance of structured testing and automating workflows to ensure seamless updates. By sharing their experiences, they aim to inspire others to adopt similar strategies for developing AI solutions. For those interested in gaining hands-on experience, a short course on evaluating AI agents is available from DeepLearning.AI and Arize.
Building an AI agent can seem like a tough challenge, especially when you’re trying to keep it running smoothly in the real world. Working with Large Language Models (LLMs) often feels like exploring uncharted waters. Even after you’ve launched an AI product, unexpected issues can pop up, pushing you back to the drawing board. Bridging the gap between development and production can get messy, but tools like Arize make things a lot easier.
This blog focuses on the journey of developing and enhancing our AI Assistant, known as Copilot. We’ve discovered a mix of features and workflows through our experiences with Arize and another tool called Phoenix that keep everything on track.
AI Agent Testing and Iterating
When we roll out new skills or testing features, Phoenix becomes our best friend. It provides insights that aid us during the development phase. We often start with a basic proof of concept to get the ball rolling. Then, it’s all about rigorous testing and iteration.
Using Phoenix, we’ve set up a testing framework that integrates Copilot components into a notebook. This makes it straightforward to run tests and immediately review the results. If data isn’t being fetched correctly or functions aren’t being called properly, Phoenix helps us spot those issues quickly. After launching new features, we turn to Arize, which helps us monitor user interactions in production. We filter data to see how things are performing, paying special attention to patterns that could signal problems.
Daily Flows: Dashboards and Monitoring
Staying on top of our AI agent’s performance involves regularly checking Arize’s dashboards. These dashboards show us key metrics, like request volume and error rates. By tracking usage trends, we can understand which features are most impactful and gather essential user feedback.
Evaluating Agent Performance with Online Evals
To keep a close eye on Copilot’s real-world performance, we set up automatic evaluations using online jobs. This helps us ensure queries are being answered accurately. The evaluation process allows us to improve our agent systematically.
Harnessing Datasets and Experiments
Daily reviews of usage data lead to new ideas and help us spot areas for improvement. We often create datasets from these insights, especially from user queries that the AI struggles with. We use these datasets for testing and to inform our development efforts.
Handling Model Switches with Experiments
When we switched to the latest model from OpenAI, GPT-4-o, we faced unexpected issues. Not all features functioned as intended, forcing us into a thorough evaluation process. Now, we leverage experiments to systematically manage these changes whenever we switch models.
Automating with CI/CD Pipelines
To streamline our processes, we’ve automated our evaluation workflows using CI/CD pipelines. This means every update undergoes testing automatically, ensuring we catch potential issues early.
Continuous Monitoring and Troubleshooting
Our evaluators continuously monitor performance once skills are live. If any skills underperform, we quickly analyze the data and make necessary adjustments directly within our testing environment.
Bringing It All Together: Code, Commit, Iterate
When we’re happy with our testing, we go ahead and update the code. The combination of tools like Phoenix and Arize keeps our development process efficient and responsive.
Ultimately, this approach helps us create a better user experience. We hope you find this overview helpful in your own development journey with AI agents.
If you want to dive into evaluating AI agents more thoroughly, consider checking out the Evaluating AI Agents course from DeepLearning.AI and Arize.
This content was sponsored by Arize. We appreciate their support in helping our community flourish.
What is an AI agent?
An AI agent is a computer program designed to perform tasks on its own. It can learn from data, make decisions, and even interact with people. The goal is to help solve problems or automate tasks in the real world.
How can I build an AI agent?
To build an AI agent, you need to follow several steps. First, define the problem you want it to solve. Then, gather data related to that problem. Next, choose the right tools and algorithms to create the agent. Finally, test and refine it to improve its performance.
What kind of data do I need?
You need quality data that relates to the task your AI agent will handle. This can be structured data like numbers and categories or unstructured data like text and images. The better the data, the more effective your agent will be.
Can AI agents interact with real-life situations?
Yes, AI agents can interact with real-life situations. They can be used in various fields like customer service, healthcare, and finance. With advanced algorithms, they can understand and respond to real-world challenges effectively.
What challenges might I face?
You might face several challenges when building an AI agent, such as collecting enough quality data, ensuring the algorithms work correctly, and managing biases in the data. It’s important to continuously test and update your agent to keep it reliable and effective.