The development of the AI agent Eris involved a significant learning curve in tool selection, moving from simple text parsing to more sophisticated methods. Initially, Eris relied on basic grep commands to parse model output for tool calls, which proved too brittle for real-world use. Subsequent iterations involved keyword lists and then embedding user messages against precomputed tool vectors, with a policy layer deciding which tools to offer. AI
IMPACT Refined tool selection in local AI agents can improve efficiency and reduce reliance on large models for decision-making.
RANK_REASON The item describes the development and evolution of a specific AI agent's tooling and selection mechanisms, rather than a new model release or core research.
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