Opening the Black Box: Mechanistic Interpretability of LLMs

As agents are deployed in high-stakes contexts (finance, manufacturing, healthcare), understanding how they make decisions—and not just what they decide—becomes fundamental to safety and trust. For example, when an agent receives the instruction "Search for our company's third-quarter results" and chooses to search internal documents instead of the public web, what internal process drives that choice? Answer engineering, behavioral testing, and chain-of-thought analysis describe correlations or narratives; none reveals the actual mechanism. Understanding how an agent reaches a conclusion is a critical component of developing AI responsibly, especially regarding reliability and transparency in AI systems. Model interpretability is one way developers can build trust and consistency in their systems and support the safe deployment of AI agents.