Executive Summary
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
Strategic AI Intelligence Breakdown
Technical & Industry Impact
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.
Market Signal & Industry Direction
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.
Tactical Advisory for Developers
By Hannes Hapke.
Strategic Context & Ecosystem Implications
As artificial intelligence continues to disrupt enterprise infrastructure, tracking individual product launches and model upgrades within their broader macroeconomic and architectural contexts is vital. Organizations deploying AI in production face rapid paradigm shifts in inference costs, API latencies, security boundaries, and sovereign regulatory compliance.
For engineering teams integrating these technologies, continuous benchmarking against competing foundation weights—and maintaining flexible model-agnostic routing harnesses—is strongly recommended to mitigate single-provider vendor lock-in.