Human-in-the-Loop

Process agents published

Also known as: HITL, Human Oversight, Human Review

Definition

A system design pattern where human judgment is required at defined points in an automated process. In agent systems, human-in-the-loop typically means: (1) approval before high-stakes actions, (2) review of outputs before delivery, or (3) intervention when the agent is stuck or uncertain. HITL trades autonomy for safety and quality control.

What this is NOT

  • Not full automation (by definition, humans are involved)
  • Not manual operation (humans review/approve, not perform every step)
  • Not just monitoring (HITL implies humans can intervene, not just observe)

Alternative Interpretations

Different communities use this term differently:

llm-practitioners

Configuring an agent to pause and request human approval before executing certain actions (e.g., sending emails, making purchases, modifying production systems) or when confidence is low.

Sources: LangChain human-in-the-loop documentation, Enterprise AI governance frameworks

ml-ops

A machine learning workflow where humans label data, validate model outputs, or provide feedback that is used to improve the model. Active learning is a specific HITL pattern.

Sources: Active learning literature, Labelbox, Scale AI documentation

Examples

  • Email agent that drafts replies but requires human approval before sending
  • Code agent that opens a PR for review rather than merging directly
  • Trading agent that proposes trades but human must confirm execution
  • Content moderation agent that flags uncertain cases for human review

Counterexamples

Things that might seem like Human-in-the-Loop but are not:

  • Fully autonomous agent that runs without any human interaction
  • Batch processing job that completes without human awareness
  • Post-hoc audit of agent actions (that's monitoring, not in-the-loop)

Relations

See Also

Implementations

Tools and frameworks that implement this concept: