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Agriculture & agritech

Agriculture and agritech increasingly run AI across the field and the supply chain, from yield prediction to autonomous machinery. Much of it is operational and lower-risk, but autonomy, safety and sustainability claims still need proportionate, demonstrable governance. Gamut lets agritech and operations teams govern this AI efficiently without over-governing the routine.

AI systems typically in scope

Section titled “AI systems typically in scope”
  • Yield prediction and crop-health models.
  • Precision-application and irrigation optimisation.
  • Autonomous and semi-autonomous machinery and robotics.
  • Supply-chain, logistics and commodity-forecasting models.
  • Sustainability and emissions-reporting AI.

Governance drivers

Section titled “Governance drivers”
  • Operational and machinery safety. Autonomous equipment carries physical risk requiring oversight.
  • Sustainability claims. Evidence behind environmental and ESG claims must be defensible.
  • Proportionality. Govern a broad estate without burdening low-risk models.
  • ISO/IEC 42001. A management-system backbone for scaling governance.

Use cases

Section titled “Use cases”

Govern autonomous field machinery

Section titled “Govern autonomous field machinery”

How Gamut solves it: register and tier the system via intake (autonomy and safety push it up), route to GTSAF and govern the agent through Agentic CISO with Gateway enforcing limits and failing closed.

Govern a broad model estate proportionately

Section titled “Govern a broad model estate proportionately”

How Gamut solves it: a deterministic governance weighting profile tiers each system, so safety-relevant autonomy gets deep assessment while routine forecasting is governed lightly.

Evidence sustainability claims

Section titled “Evidence sustainability claims”

How Gamut solves it: capture the evidence behind ESG and sustainability claims through the Evidence Tracker and report it via reporting.

Quick start

Section titled “Quick start”
  1. Inventory the estate in AI System Records.
  2. Run intake; let weighting tier each system proportionately.
  3. Route high-impact systems to GTSAF and ISO/IEC 42001; govern machinery through the agentic stack.
  4. Evidence safety, accuracy and sustainability in the Evidence Tracker.
  5. Track gaps on the Remediation Roadmap.
  6. Report posture from reporting.

Frameworks that apply

Section titled “Frameworks that apply”

GTSAF, ISO/IEC 42001, NIST AI RMF, and for autonomous machinery ATF and MAESTRO.

Next

Section titled “Next”