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This article focuses on OpenClaw's integration of decision models to reduce reliance on language models for routine agent logic, thereby improving efficiency and cost. By embedding deterministic criteria directly into application code rather than relying solely on LLMs for every judgment, developers can create faster and more scalable AI agents. This approach represents a significant architectural shift in how machine learning systems handle tool selection and state management within autonomous workflows.
Decision models in OpenClaw
Josh Lehman on what decision models make possible, why OpenClaw is taking a plugin-first approach, and where contributors can help.
When building an agent, there are a lot of decisions that don’t need to be a conversation. Which tools are relevant to this request? Which messages should survive compaction? Is somebody actually talking to the agent, or should it stay quiet? We can ask a language model to make those decisions, but doing so adds time and cost to the work the user actually wanted done. As agents take on more of these small judgments, those extra calls start to matter. Last week, Jev made a lot of developers rethink that tradeoff. Today, Josh Lehman, an OpenClaw maintainer, shares why decision models caught his attention, how we’re bringing them into OpenClaw, and why some of the most interesting work is going to come from the community. The magic is when you take Jev and embed it within normal deterministic code. The magic…
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