Honey Nudger
Recursive Self-Learning For Agents
What it does
Most AI agents work in the lab, then fail in production. The fix? Manual annotation, endless prompt tuning, and praying. We built Honey Nudger to move the industry from prompt engineering to "performance engineering." Instead of hand-labeling edge cases, your AI agents learn continuously from their own experience — self-improving based on what actually moves the metrics you care about. Stop tuning. Start learning.
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We implemented Stanford's Agentic Context Engineering paper which shows agents can improve their performance just by evolving their own context. How it works: Agents execute tasks, reflect on what worked/failed, and curate a "playbook" of strategies. All from execution feedback - no training data needed. Happy to answer questions about the implementation or the research!
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Launched alongside, March 2026
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