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;…
- 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.
- 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.
Covered in Phase 11: LLM Engineering, Phase 14: Agent Engineering and Phase 15: Autonomous Systems.
Related terms
- 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.
More terms in Agents & tools
- Agent
- Agent Harness
- Agent Memory
- Agent State
- Agent Skill
- Approval Gate
- Checkpoint
- Compensating Action
- Delegation
- Durable Execution
- Function Calling
- MCP (Model Context Protocol)
- Multi Round-Trip Request (MRTR)
- Orchestration
- Planning
- ReAct
- Sandbox
- Skill Bundle
- Skill Catalog
- Skill Discovery
- Skill Invocation
- Stateless MCP
- Structured Output
- Swarm
- Termination Condition
- Tool Contract
This entry comes from glossary/terms.md on GitHub. Browse all 250 glossary terms.