01
Equipment maintenance triage
An agent reads sensor and usage data across equipment, flagging maintenance needs before failure with reasoning attached.
Agentic AI in agriculture
Agents for the coordination gap in field operations.
We build agents that read equipment, procurement, and compliance data across distributed field operations — with the audit trail your operations team can actually trust.
Trusted by operators
-
+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
Read case study -
Supply chain intelligence agents reducing manual coordination overhead.
-
37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
-
AI agent implementation for a global automotive enterprise.
-
2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
Read case study -
0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
Read case study
Why agentic AI
Field data does not turn itself into a decision
Sensor readings, equipment logs, supplier documents and compliance records arrive from separate places and rarely line up on their own. Most operations already collect all of it. The work that stays manual is connecting that data to a decision, with someone cross-referencing every source by hand.
every recommendation traces to its source data
Use cases
Where agents earn trust in agricultural operations
Distributed, multi-source workflows with a clear escalation path.
02
Input & supply procurement documents
Reads supplier documents and purchase records, cross-checks against contracts, and drafts orders for a human to approve.
03
Compliance record-keeping
Maintains sourced, auditable records across distributed field operations for regulatory and certification review.
04
Yield and forecast reconciliation
Cross-references incoming yield data against forecasts, surfacing the exceptions that need agronomic attention first.
What the mechanism delivers
Cross-system coordination, measured in production
None of these numbers come from agriculture. Vstorm has not shipped a production agent inside an agriculture business, so every figure below is from another industry — Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare). They are here because they measure the three things an agronomy or supply-coordination workflow depends on: a validated multi-step run instead of a single pass, orchestration across a distributed operation, and staged validation before a recommendation is acted on. Every figure links to the case study behind it.
Agents do not set an application rate, approve a purchase order, or sign off a certification record. They gather, cross-check and draft what sits before an agronomist's or operations lead's decision. Every step is logged for review.
Manual setup per multi-step workflow
2 hrs
Not an agriculture deployment — engineers on Synera's platform assembled each complex workflow by hand, node by node.
95.4%
Success rate in workflow results
A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
0 hallucinations
across 329+ nurse-validated scenarios
Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
Client results
Proof from production agentic deployments
We have not yet shipped a production agent inside an agriculture business. These are the closest available references — the same multi-step workflow generation, distributed-operation coordination and staged validation, shipped in engineering software, print on demand and healthcare.
Delivery path
From workflow audit to a production agent
TriStorm keeps data quality and engineering aligned — integration and data-quality risks surfaced before full build commitment.
Map systems and data sources
We audit target workflows, field-system boundaries, and data quality — ranking automation candidates by impact and integration risk.
- Systems & data audit
- Data quality review
- Prioritised use case
Build and validate the agent
We implement against real field and procurement data shapes, with an evaluation suite scored before any output reaches an operator.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout with monitoring and audit logging, plus a structured handoff so your team runs the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own agronomy and supply-coordination workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in agriculture, answered
Where does agentic AI actually help in agricultural operations?
In workflows that combine field-distributed sensor or yield data with a decision that needs cross-referencing — input procurement, equipment maintenance scheduling, or compliance documentation. We build agents for that coordination layer, not to replace agronomic judgment.
How is this different from the dashboards and alerts we already have?
A dashboard shows you a signal. An agent reads the signal alongside other data (equipment logs, weather, supplier documents) and completes a multi-step task: drafting a purchase order, flagging a maintenance need, escalating a compliance gap.
What agricultural workflows are realistic first projects?
Equipment maintenance triage from sensor and usage data, input and supply procurement document processing, and compliance record-keeping across distributed field operations.
Can an agent work with data spread across disconnected field systems?
Yes — this is fundamentally a retrieval and integration problem: an agent reads from multiple systems of record and reasons across them, which is the same mechanism we have engineered for other multi-system, document-heavy operations.
What is the typical timeline to a working system?
A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement.
Do we own the system after it is built?
Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in, and your team is trained to operate and extend it.
Start with one workflow
Map one field operations workflow worth automating
A 30-minute call identifies data sources, integration points, and a realistic path to a working agent.