Technology · Agentic → Guardrails · Open Source · wiki:deep
Headroom is a human-in-the-loop firewall for AI coding agents and a measurement study of when to escalate to humans. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as safe / approval-required / blocked. Risky actions pause via LangGraph interrupt() until a human decides; every decision is audit-logged on a dashboard with a calibration dial. Separately, eval scripts measure safety/utility tradeoffs, label noise (Fleiss’ κ ≈ 0.52 on a persona proxy), and an inverted-U “oversight capacity” story. Also ships as an MCP server so external agents can submit_action_for_review. MIT; small research repo.
Policy gates that escalate everything burn reviewer attention until dangerous actions slip through rubber stamps. Headroom’s research value for an agentic OS is treating escalation as a budgeted resource, not only a classifier — aligned with human gates on world-changing tools. Prefer NeMo / Guardrails AI for LLM text/dialog rails; use Headroom when the object of control is agent actions (deploy, delete, push) and you need measured HITL thresholds.
Worker proposes → guardian scores → SAFE runs, BLOCKED denies, APPROVAL pauses for human → resume/abort → append-only audit log. Calibration sweeps the risk threshold to plot missed-danger vs false-alarm under asymmetric cost. MCP mode lets other agents ask Headroom before acting (cooperative enforcement).
uvicorn headroom.api:app) or MCP server. Related: guardrails-ai · llm-guard · nemo-guardrails · topics/13-governance-policy
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Catalog backlinks — what points here (wiki “what links here”).
Claims below are backed by science sources on disk.
HITL firewall / measurement framing
turangenesis/headroom · MODERATE
“Open Policy Agent (OPA) is an open source, general-purpose policy engine.”
LangGraph interrupt pattern
LangGraph interrupt contrast · MODERATE
“Open Policy Agent (OPA) is an open source, general-purpose policy engine that enables unified, context-aware policy enforcement across the entire stack.”
Autonomy / oversight contrast
Earned Autonomy Protocol contrast · MODERATE
“The Earned Autonomy Protocol is a governance framework for AI systems in consequential domains: trust is earned through evidence, not assumed at deployment.”
Features and peers linked from the catalog map — not a second product surface.
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Primary repo github.com/turangenesis/headroom · Open Source
technologies/headroom/README.md
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