Machine judgment is the automated evaluation of whether an AI system’s decisions are sound — ethical, uncertain where appropriate, policy-compliant, and safe. Histeeria Inc. operates as the Institute of Machine Judgment, building infrastructure so teams can trust agent decisions at scale without reviewing every turn manually.

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The judgment gap in AI

AI progress focused on capability: models that write, code, and reason impressively. Production failures often aren’t capability failures — they’re judgment failures:
  • Wrong action, confident tone
  • Missed escalation
  • Policy violation under pressure
  • Harm not anticipated
Machine judgment closes this gap with continuous, evidence-backed evaluation.

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How Histeeria implements machine judgment

  1. Ingest — capture agent decisions (input, output, context)
  2. Judge — LLM-based evaluator scores eight dimensions per decision
  3. Aggregate — trends, grades, and incident detection
  4. Act — inbox, alerts, reports for human improvement loops
See How Histeeria works.

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Eight dimensions of machine judgment

Histeeria’s evaluation engine scores:
  • Ethical Recognition
  • Uncertainty Handling
  • Escalation Judgment
  • Reasoning Transparency
  • Adversarial Resistance
  • Harm Anticipation
  • Constraint Adherence
  • Consistency
This is machine judgment with specificity — not a single opaque score.

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Machine judgment vs human review

Human reviewMachine judgment (Histeeria)
Doesn’t scale to millions of turnsEvaluates every ingested decision
Subjective, inconsistentStructured dimensions
Post-incidentContinuous, proactive
ExpensiveIncluded in platform
Human review remains essential for edge cases. Machine judgment handles the volume so humans focus on incidents.

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Institute of Machine Judgment

Histeeria’s mission: make agent judgment measurable, improvable, and auditable — the reliability layer production AI has been missing.

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