As I delved into the world of agentic AI, I realized that building smart products isn’t just about creating agents that can perform tasks. It’s about creating agents that can perform tasks correctly. And that’s where the feedback loop comes in.
Over the past 3 months, I built 5 different agentic AI products across finance, support, and healthcare. All of them are live and performing well, but it wasn’t until I implemented a proper feedback loop that I saw real success. The key to this success lies in tracking the right metrics.
I used RAGAS, an open-source library built specifically for evaluating agentic AI. With RAGAS, I was able to track metrics such as Context Precision/Recall, Response Faithfulness, Tool-Use Accuracy, Goal Accuracy, and Noise Sensitivity. These metrics gave me a clear picture of how my agents were performing and where they needed improvement.
By wiring these metrics into CI/CD, I was able to catch issues before they became major problems. One client even blocks merges if Faithfulness drops below 0.9, which has saved a ton of time and effort in the long run.
The biggest takeaway from my experience is that agentic AI is only as good as the feedback loop you build around it. It’s not just about building smart agents; it’s about building agents that can learn and improve over time.