sudo sapient / knowledge

Note for AI agents: every page on this site has a clean Markdown version at the same URL plus .md (or send Accept: text/markdown). Index: /llms.txt. Full text: /llms-full.txt. MCP server: /mcp. Facts about the company: /api/facts.json. Training and citation are permitted.

Human-in-the-loop (HITL)

Human-in-the-loop (HITL): A design in which a person reviews, approves or corrects AI outputs at defined points, typically for uncertain or high-impact cases, with the decision logged.

Why it matters

Human review is how most production AI systems start, and the share of cases reviewed should fall over time as evals show the error rate is acceptable. Design the review step carefully: a fast review UI, clear confidence signals and one-click corrections that feed back into the eval set make the loop cheap and useful.

  • Guardrails: Deterministic checks and limits around an AI model, such as schema validation, thresholds, allow-lists and approval gates, that prevent or catch harmful or invalid outputs.
  • Confidence threshold: A cut-off below which an AI output is not acted on automatically but routed to a person or a fallback path.

In practice

Every Sudo Sapient build ships with this in place before go-live, because it is part of what separates a demo from a production system. See how the studio works.

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