Definition

Human in the loop (HITL)

An AI setup where a person reviews, corrects, or approves the system's work as part of the process.

How founders on the podcast use it

A system design where people review or approve what AI produces.

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A person who reviews, approves, or can override an AI system's decisions.

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In Shar's usage, specifically means humans own judgment and decision gates, not that a human glances at the output.

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A workflow requiring human review or approval at defined checkpoints.

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Keeping a person in the decision so the machine learns from expert judgment.

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A design pattern where AI proposes an action but waits for human approval before executing it — Unthread's safety gate for destructive or sensitive operations.

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Keeping a person involved in AI-driven decisions and output. Dobbin proposes amendments to the Foundation; the founder accepts or rejects them.

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An AI workflow design pattern in which human review, approval, or oversight is embedded at defined decision points within an automated process — not as a fallback when AI fails, but as a structural component of the architecture. In high-stakes environments (healthcare, finance, legal), HITL is not a workaround for AI limitations; it's the accountability mechanism the AI cannot yet provide for itself. The human's role is not to replace the agent's computational capacity but to verify outputs at the moments where hallucination risk is highest.

10 Million Americans: Healthcare Behind the Scenes

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Related terms in Agents & Automation

End-to-End Process AutomationAutomating an entire business process from input to output, rather than discrete tasks within it. The core EvoluteIQ thesis.Governed agentsAI agents running under defined rules, permissions, and data protections.HarnessThe organizational scaffolding, tooling, and process layer wrapped around AI models to make them useful and swappable.Human AI TeamThe near-future organizational unit in which AI is a genuine collaborator integrated into daily work — not a tool occasionally consulted. Feedback flows in both directions: humans evaluate AI outputs, AI evaluates human reasoning and approach, and there may be an AI evaluating how well the human evaluates AI. Geoff's view: the teams that win in the next decade will be the ones who redesign around this model — not just buying tools, but genuinely reinventing how humans and AI work together to achieve outcomes neither could reach alone.Human-in-the-Loop AIAn AI system design pattern in which humans remain involved in the workflow to review, correct, or approve AI outputs before they drive consequential actions - rather than operating fully autonomously. Human-in-the-loop is distinct from fully automated AI (where no human reviews outputs) and from purely human-operated workflows (where AI provides no assistance). Mehul Shah's key refinement: the goal is to keep humans in the loop for decisions while removing them from the critical path of execution. The human should not be the bottleneck who processes every document - that is the work AI automates. The human should be the final authority who reviews flagged items and makes the consequential calls.HyperautomationAn earlier wave that combined RPA, workflow tools, and basic AI to automate as many processes as possible. Now considered a precursor to agentic automation.In-House AgentKindling Solutions's smaller entry point: one AI role installed to solve one defined problem.Knowledge Base TrainingThe process of feeding audio recordings, sales scripts, and objection handling examples into an AI agent so it adopts the cadence, tone, and approach of a specific human salesperson.