🍃Evaluation

In a Noisy AI World......

Lady H. had traveled across countless planets in the cosmos. No matter how different their worlds or evolutionary paths were, they all eventually invented AI to replace much of human effort. This time, she is traveling to Earth for a one-year vacation, there, the AI revolution just started. Once again, she's witnessing the same madness unfold.

Giant companies are pouring vast sums of money and resources into building their own LLMs (large language models). The entire architecture of software development was being reshaped, with AI expected to appear in almost every corner of technology, and agentic AI systems are becoming the new obsession.

Enormous investments flowed into AI. Many people lost their jobs as automation spread. Some corporate leaders even dreamed of eliminating humans from the workflow entirely, replacing them with AI systems. Overpromising became rampant, fueled by an overwhelming confidence in the power of AI.

Why Evaluation

Lady H. believes that the madness will eventually settle once the world demands real profits from AI. When that moment comes, success will no longer depend on how many agentic AI platforms or LLMs exist, but on how effectively these technologies are deployed into products that real people actually use.

No matter how powerful the models become, one process remains essential across all platforms — evaluation. Evaluating an AI system's performance, confidence, cost, efficiency, and other key metrics is what separates genuine innovation from mere hype. Only through rigorous evaluation can AI move from dazzling experiments to dependable product that create real value.

And, Why Auto Evaluation

At the same time, it's nearly impossible to build a perfect product in one go. Instead, great products are created through continuous improvement: launching an initial version, gathering user feedback, making enhancements, and releasing new versions. What makes this process effective is evaluation through version comparison — testing multiple versions of a product and selecting the one that performs better.

In agentic AI systems, evaluation can happen at any stage of the process. This flexibility is one of the key advantages of agentic AI. To keep the entire system running smoothly, an auto evaluation framework plays a crucial role, ensuring the validation step is guided by data-driven insights rather than guesswork.

Now, follow us to take a look at a customizable auto evaluation framework Lady H. has developed.