Presentation: Opening the Black Box: Critical Explainability for AI Agent Tool Selection with Kiji Inspector
Hannes Hapke shares how Dataiku’s open-source Kiji Inspector applies Sparse Autoencoders (SAEs) directly to decision tokens to expose why agents pick specific tools. Moving past superficial chain-of-thought narratives, he discusses causal ablation, layer activation extraction via patched vLLM engines, and production monitoring workflows for software architects and senior engineering leaders. By Hannes Hapke
Hannes Hapke shares how Dataiku’s open-source Kiji Inspector applies Sparse Autoencoders (SAEs) directly to decision tokens to expose why agents pick specific tools. Moving past superficial chain-of-thought narratives, he discusses causal ablation, layer activation extraction via patched vLLM engines, and production monitoring workflows for software architects and senior engineering leaders. By Hannes Hapke
