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Anthropic's Claude has been tested on safety benchmarks for common misalignments like deception and sycophancy, focusing on preserving capabilities and evaluating method generalization.
People want to build agents easily and have them improve over time; automating the 'hill climbing' loop is hard but has high ROI.
This blog post by Sydney Runkle explains the art of loop engineering for building reliable LLM agents using LangChain primitives, covering four levels of loops: agent loop, verification loop, event-driven loop, and hill climbing loop.