Model Hardware Standard (MHS)

Anthropic's specification letting AI agents safely operate physical lab and manufacturing hardware, extending agent tool-use to the physical world.

Anthropic · Agent frameworks and orchestration · framework

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Overview

Announced August 27, 2026, the Model Hardware Standard (MHS) is a shared specification that lets AI agents read sensors, write to actuators, and operate physical equipment such as microscopes, liquid handlers, robotic arms, and lasers through a common interface, cutting hardware integration time from weeks or months down to hours or minutes. Anthropic opened a research preview with named partners Genentech, the University of Washington's Baker and Pinglay labs, Carnegie Mellon University, HHMI Janelia Research Campus, QuEra Computing, and Tetsuwan Scientific. Early results include QuEra improving laser-lock recovery success from 58% to 99.3% (cutting recovery time from 150 seconds to 6 seconds) and Carnegie Mellon cutting serial-dilution experiment time roughly 3x while achieving curve-fitting accuracy above R² 0.98. Anthropic plans to open source the full specification after developing safety evaluations and best practices with launch partners.

This is a sourced directory entry. Features and capabilities are reported in the linked sources; this entry does not document independent Aymorphic testing.

Key features

  • Common interface for AI agents to read sensors and operate actuators on lab/manufacturing hardware
  • Cuts hardware integration time from weeks/months to hours/minutes
  • Enables collision-free, parallel operation of multiple instruments (demonstrated at University of Washington)
  • Research preview partners: Genentech, University of Washington, Carnegie Mellon, HHMI Janelia Research Campus, QuEra Computing, Tetsuwan Scientific
  • QuEra Computing: laser-lock recovery success up from 58% to 99.3%, recovery time cut from 150s to 6s
  • Full specification planned to be open sourced after safety evaluation with launch partners

Use cases

  • Automating drug-discovery and liquid-handling lab experiments (Genentech)
  • Serial-dilution experiment automation with curve-fitting validation (Carnegie Mellon)
  • Precision calibration tasks such as quantum-computer laser locking (QuEra Computing)
  • Collision-free multi-instrument coordination in research labs (University of Washington)

Pricing

unknown (research preview with a vetted partner cohort; no public pricing disclosed).

Pricing reflects the source record and may change. Confirm current terms with the provider.

Dates and sources

Released
27 Aug 2026
Record updated
23 Sept 2026
Record last checked
23 Sept 2026

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