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Most HITL implementations fail in production. Approvers rubber-stamp decisions, alert fatigue sets in, and oversight becomes theater. This course teaches the patterns that work at scale: filtered approval gates, sampling strategies, escalation paths, and feedback loops that compound over time.

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1. 01

   Start here: what this course is about

   Welcome to Advanced HITL Patterns

   Read this first. ~3 minutes.

2. 02

   Foundations: HITL cycle review

   What is Human-in-the-Loop (HITL)?↗

   Quick recap of the HITL cycle before going deep. The propose-review-correct-learn loop.

3. 03

   The 3 core HITL workflow patterns

   Human-in-the-Loop Patterns: Approval, Input, and Escalation Workflows↗

   Approval, input, and escalation: the three workflow patterns every HITL system uses. Clear breakdown.

4. 04

   Designing approval gates for production

   AI Human in the Loop: Production Oversight Patterns - Redis↗

   Production oversight patterns. State management for approvals, durable workflows, kill switches.

5. 05

   Sampling and auditing at scale

   Challenges and Solutions to Scaling HITL AI Evaluation↗

   How to evaluate AI quality at scale without reviewing every output. Statistical sampling, golden sets, rubric design.

6. 06

   The 3-layer quality system

   Evaluation at Scale: Combine Golden Sets, Sampling, and Human Review to Keep Gen AI Quality High↗

   Golden sets + production sampling + human review with rubric. The 3-layer quality system that works.

7. 07

   Feedback loops that improve the system

   Incorporating Human-in-the-Loop Feedback for Continuous Improvement of AI Agents↗

   Capture every correction. Use it for eval. Recalibrate thresholds. Reduce escalations over time.

8. 08

   Escalation paths and active learning

   Active Learning: Selectively Labeling Informative Samples (research overview)↗

   Active learning research: how to selectively pick the cases that teach the AI the most. The math of efficient feedback.

9. 09

   HITL patterns for agentic workflows

   Designing Human-in-the-Loop for Agentic Workflows↗

   Multi-step agents need HITL at decision junctions, not after every step. Where to gate, where to let the agent run.

10. 10

    Evolving HITL as systems mature

    Human-in-the-loop has hit the wall. It's time for AI to oversee AI↗

    Critical view: HITL was built for discrete decisions. When and how to move toward HOTL or AI overseeing AI.

11. 11

    State of the art: where the human role goes

    The Coming Loop↗

    Armin Ronacher on harness loops and the diminishing role of human judgment in autonomous AI systems. Required reading after the rest.

12. 12

    Closing: what to take with you

    Closing: HITL Patterns You Now Have

    Wrap-up. ~3 minutes.

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