Agents & tools · Glossary term

What is Human-in-the-Loop (HITL)?

A workflow design in which a person supplies judgment, correction, approval, or escalation at defined points in an AI-driven process.

Also called Human oversight and human review.

Why does Human-in-the-Loop (HITL) matter?

Human involvement is most useful at high-impact, ambiguous, or irreversible boundaries, not as an undefined fallback after every step.

Human-in-the-Loop (HITL) in practice

Let the agent classify routine requests automatically, but route uncertain or high-value cases to a reviewer with the evidence and proposed action.

What is the common confusion about Human-in-the-Loop (HITL)?

HITL does not automatically make a system safe. Reviewers need time, context, authority, and a clear decision standard.

Learn Human-in-the-Loop (HITL) in the course

Lessons that name Human-in-the-Loop (HITL) in a title or section

  • Human-in-the-Loop: Propose-Then-Commit

    The 2026 consensus on HITL is specific. It is not "the agent asks, the user clicks Approve." It is propose-then-commit: the proposed action is persisted to a durable store with an idempotency key;…

    Phase 15: Autonomous Systems

  • Agent State Machines — Graphs, Nodes, Checkpoints

    A ReAct loop written by hand is a while True. The same loop written as an explicit graph is something you can checkpoint, interrupt, branch, and time-travel through. The agent hasn't changed.

    Phase 11: LLM Engineering

  • Stateful Graph Orchestration — Durable Execution and Checkpoints

    Agent is a state machine; nodes are functions; edges are transitions; state is checkpointed after each node. Resume from any failure at the last successful checkpoint.

    Phase 14: Agent Engineering

Covered in Phase 11: LLM Engineering, Phase 14: Agent Engineering and Phase 15: Autonomous Systems.

  • Approval GateA control point that blocks a consequential action until an authorized person or policy grants permission.
  • Verification GateA control point that blocks progress until defined evidence satisfies a correctness or quality criterion.
  • AgentA software system that lets a model select actions toward a goal, observe tool or environment results, and continue under an orchestration…
  • GuardrailsSystem controls that constrain inputs, tool use, outputs, permissions, and escalation.
  • AI Risk AssessmentA documented analysis of how an AI system can affect people, organizations, and environments, including context, hazards, likelihood,…
  • AlignmentThe effort to make a model or AI system behave in ways that match intended goals, constraints, and human preferences across both expected…
  • Multi Round-Trip Request (MRTR)An MCP request pattern in which an operation returns `resultType: input_required` with one or more `inputRequests`, then the client…
  • Test OracleThe mechanism, specification, reference, invariant, or human judgment used to decide whether observed program behavior is correct.

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