Continuous feedback and evaluation infrastructure for AI agents
Build infrastructure that records agent interactions, associates later scores or comments with each run, produces candidate updates, evaluates them, and publishes only updates that pass a policy. Separate evaluation tooling can compare models by accuracy, response time, and cost using field-specific scoring rules.
From Github Awesome — GitHub Trending Today #48: reverify, keslr_connect, anti-slop, boardui, ffmpeg-skill, chippytea at 05:40
Problem: Agent improvements need to be based on real feedback and released safely, while model comparisons need task-specific scoring rather than a single generic metric.
For: Teams operating AI agents or comparing models for production workflows.
Products from this video
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Examples
- Reef: can update model weights or an agent's prompts, rules, and skills.