Human-in-the-Loop Systems
Build hybrid systems where agents automate routine work and humans handle complex decisions
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0 / 5 completedClosing the Loop: Learning from Human Feedback
The true power of human-in-the-loop isn't just human oversight—it's continuous learning. Every escalation, approval, and correction becomes training data that makes the agent smarter and more autonomous over time.
The Feedback Learning Cycle
Based on current knowledge, escalates uncertain cases
Approves, rejects, or corrects the decision with reasoning
Feedback becomes training data, models update, confidence improves
Agent handles more cases confidently, escalation rate decreases
Interactive: Feedback Learning Simulator
Watch how human corrections train the agent. Each time you apply feedback, the agent's confidence and accuracy improve:
Agent deliberately escalates edge cases it's uncertain about to gather training data in weak areas
Models are retrained regularly on human feedback, improving decision quality without code changes
Over time, agent handles more autonomously. Week 1: 40% escalation. Week 12: 8% escalation. Same quality.
Agent learns when it's truly confident vs overconfident, reducing both false positives and unnecessary escalations
Human-in-the-loop isn't a permanent state—it's a training process. The goal is to gradually shift more responsibility to the agent as it learns from human expertise. Ideally, escalation rates drop from 40% to under 10% while maintaining or improving decision quality. Humans transition from doing the work to teaching the agent to do the work.