🧑‍⚖️ Human-in-the-Loop Design Pattern for AI Agents

Описание: Human-in-the-Loop Design Pattern

Chapter:
00:00 Introduction
00:50 Human-in-the-Loop Design Pattern - architecture
01:53 Core idea of Human-in-the-Loop Design Pattern
03:00 Why Human-in-the-Loop Design Pattern matters
03:57 When to use Human-in-the-Loop Design Pattern
04:43 Limitations of Human-in-the-Loop Design Pattern
06:09 Examples in Bioinformatics domain
07:07 Hands on (Environment setup)
07:30 Define Agents
08:03 Connect agents in a Human-in-the-Loop workflow
09:16 Execute the pipeline
10:39 Summary and recap

Code: https://github.com/prpanigrahi/ai_age...

In this video, we explore the Human-in-the-Loop (HITL) Design Pattern, a critical approach for building AI agents that combine machine efficiency with human judgment 🤖➡️🧠.

Not all decisions should be fully automated. In high-stakes systems, AI agents handle routine work autonomously but pause at critical checkpoints to involve human reviewers. Humans evaluate results, provide guidance, or grant approval before the agent continues.



🧠 In this video, you’ll learn:
✅ What the Human-in-the-Loop design pattern is
✅ How agent workflows pause, escalate, and resume
✅ Why accountability and oversight matter in AI systems
✅ When HITL is mandatory — and when it’s unnecessary
✅ Trade-offs in speed, complexity, and system design

📌 Human-in-the-Loop is especially important when:
• Decisions have safety, ethical, or legal implications
• Errors carry significant consequences
• Subjective or contextual judgment is required

🧬 We also discuss bioinformatics and life-science use cases, such as:
• Clinical variant interpretation approval
• Diagnostic decision-support systems
• Regulatory or compliance reporting
• Genomic analysis pipelines requiring expert review

⚠️ Full automation isn’t always the goal. The best systems balance autonomy with accountability.

👉 This video concludes the Agentic AI Design Patterns series. Use these patterns as a practical toolkit for designing reliable AI agents.