Andrej Karpathy’s analysis reveals a significant limitation in relying on agent skills for AI-driven workflows: their struggle to maintain accuracy in complex, multi-step tasks. As outlined by The AI Automators, these skills often rely on probabilistic models, which can result in skipped steps or hallucinated outputs. For instance, in areas like regulatory compliance or medical diagnostics, even small errors can lead to serious consequences, highlighting the need for more reliable approaches. Deterministic harness engineering offers a structured alternative, using mechanisms like validation loops and state tracking to ensure outputs remain precise and dependable. Dive into this deep dive to understand how harness engineering addresses these challenges through specific features such as context isolation and error correction. Explore real-world examples, including Stripe’s “minions” and Anthropic’s plugins, which showcase the practical application of harnesses in managing intricate workflows. Gain insight into how these methods can be adapted for tasks like financial audits or contract reviews, equipping you with strategies to design AI systems capable of meeting high-stakes demands. The Reliability Problem in AI TL;DR Key Takeaways : …