Staged Deployment
Before any model reaches the field, we expose it to small, controlled operator groups. Real environments. Real edge cases. We observe, measure, and iterate before widening access — because agriculture tolerates no surprises.
Safety Evaluations
We run layered evaluations — automated benchmarks and human review — to verify that every model release meets our internal safety thresholds. Models that fail are held, not shipped.
Red Teaming
We work with external domain experts, independent researchers, and trusted operator partners to actively attack our systems. That means adversarial inputs, data poisoning attempts, manipulation of sensor streams, and probes for unexpected behavior under degraded conditions. Findings feed directly back into model hardening and guardrail development.
Preparedness Framework
Before releasing any new AI capability, we assess it across a set of tracked risk categories. If a model registers High capability in any category, we strengthen safeguards before it ships — no exceptions.
Red Teaming
We Stress-Test Before You Rely on It
Our red teaming practice brings in people who don't work here — agronomists, security researchers, adversarial ML specialists, and field operators — to find what we missed. We give them real model access and ask them to break things.
Their findings go directly into our guardrail development and inform which capabilities get delayed, restricted, or redesigned. No finding is suppressed. The point is to identify risks before the world does.
Preparedness Framework
Tracked Risk Categories
Every new capability we develop is evaluated against a defined set of risk categories before it ships. A High capability rating in any category triggers mandatory safeguard work — deployment is gated until the risk is addressed.
Biosystems & Environmental
AI that interfaces with living systems — crop health, soil chemistry, pest detection — must not cause irreversible ecological harm. We evaluate for unintended cascade effects, misclassification under distribution shift, and failure modes that could damage crops or contaminate ecosystems.
Cybersecurity & Infrastructure
Farm infrastructure is increasingly connected. We assess whether our models could be weaponized to disrupt irrigation systems, falsify sensor data, or enable lateral movement into operational networks. We test for adversarial robustness and build against it.
Autonomous Decision-Making
Agentic systems that act in the physical world — drones, precision applicators, autonomous field vehicles — must have bounded decision authority. We evaluate whether models attempt to expand their own operational scope and impose hard constraints against self-directed escalation.
Ongoing Work
Safety Doesn't Ship Once
Every deployment generates signal. Operator feedback, anomaly detection, and model monitoring feed a continuous loop of evaluation and improvement. We review emerging findings and update our policies as capabilities and threat models evolve.
Our safety work isn't a team — it's a property of how the entire company operates. Every engineer is accountable. Every model has an owner. Every incident has a postmortem.
Work with us on safety
We're looking for researchers, domain experts, and operators who want to help us find failure modes before they happen. If that's you, reach out.