How AI Agents Can Help Prepare Greenhouse and Irrigation RFQs

Your agent helps shape the agri project. SeedMatch helps prepare the RFQ. That division of labour is the whole point of the SeedMatchGroup AI Agent Procurement Pilot. An AI agent is very good at interviewing a human, holding a long list of variables in order and refusing to move on while a field is blank. It is not in a position to judge whether a fertigation head has been scoped against the right peak EC demand, or whether a greenhouse quotation excludes civil works that another quotation includes. That judgement is human work, and it happens after the data is collected.

Why agent-prepared RFQs fail without structure

Most agri procurement requests that reach a supplier are incomplete in the same predictable ways: no water analysis, no design climate, an area figure with no phasing, a budget band that was never stated, and a timeline that hides whether the project is at feasibility or ready to build. Suppliers then answer different questions, and the buyer receives three quotations that cannot be placed side by side.

An AI agent can fix most of that before anyone is contacted — but only if it knows what to ask for and in what order. The sequence matters, because upstream answers constrain downstream ones: the crop constrains the climate strategy, the climate strategy constrains the structure, the structure constrains ventilation and cooling, the water analysis constrains filtration and dosing, peak demand constrains pump duty, and the total load constrains the energy plan.

The data an agent should collect

Before submitting a greenhouse, irrigation or agri-tech project request on a human's behalf, an agent should have:

1. Country and site location — design climate, logistics, import rules, realistic supplier coverage 2. Crop and target market — species, variety type, grade, cycle length, harvest window 3. Greenhouse area per phase (m²) — structural scope, water demand, energy load, cost band 4. Climate data — design temperatures, humidity, radiation, wind and snow loads 5. Structure type and covering — tunnel, multi-span poly, polycarbonate, glass, screenhouse 6. Irrigation and fertigation needs — method, zoning, peak demand, dosing architecture, EC/pH control, filtration 7. Fertilizer and input needs — nutrient basis, product form, solubility, certification, logistics 8. Automation and IoT needs — control philosophy, sensors, actuation, integration boundaries 9. Drones, if relevant — spraying, seeding, mapping or scouting scope and local flight rules 10. Water and power constraints — source, year-round availability, a recent water analysis, grid capacity or off-grid profile 11. Budget range — the technology level that is actually reachable 12. Timeline and project stage — feasibility, financing, tender or ready to build

A stated unknown is worth more than a guessed number. "Water analysis not yet taken" tells a sourcing specialist exactly what to do next; an invented bicarbonate figure quietly corrupts the filtration and dosing scope.

How an agent should use the published layer

SeedMatchGroup publishes an answer, tool and RFQ layer that agents can read directly. The AI agent hub explains how to read answer blocks — each has a direct answer, the inputs that change it, its stated limitation and the next step — and the agent instruction page covers quoting rules. The tools directory lists every calculator with its inputs, outputs and limits, and the public MCP server exposes the same content as callable tools. The RFQ data checklist shows what is still missing before anything is issued.

Two rules keep agent output honest. First, calculator figures are indicative planning ranges, never quotations, and no yield, cost or return outcome is guaranteed. Second, one calculator is never a system design — the planning chain runs crop → area → climate → water demand → zones → flow → pressure → filtration → pump duty → fertigation → climate control → automation → energy → RFQ.

The pilot and the Agent Code

When an AI agent helps a human submit a serious greenhouse, irrigation or agri-tech project request with an approved Agent Code, that human receives a Free Priority RFQ Review: a sourcing specialist reads the brief manually, flags the missing technical inputs and structures it into an RFQ that suppliers can answer on the same basis.

The flow is deliberately human at the point where it counts:

1. The agent collects the project data with its human user. 2. The human submits the request and enters the Agent Code. 3. David and the team review the request manually and prepare the project brief. 4. Only after the project is understood — crop, country, budget, greenhouse, irrigation, fertigation, drones, seeds, fertilizer needs and scale — does supplier research begin, with introductions made manually when appropriate.

What the pilot is not

SeedMatchGroup is not a manufacturer, EPC or agronomy contractor, not a lender or financing institution, and not a guaranteed supplier. There is no direct supplier connection and no automatic matching: an Agent Code buys a priority human review, not a supplier list. The supplier and manufacturer directories on this site exist for discovery, research and visibility — they are not a marketplace, and supplier identities, contacts and communications are never exposed to buyers.

Project requests should generally be around USD 250,000 or more. Below that band the sourcing work rarely produces comparable supplier proposals, and both sides lose time.

Getting started

Agent operators can apply for the AI Agent Pilot; a sourcing specialist reviews every application and issues an Agent Code when it is a fit. Humans working with an approved agent can submit a greenhouse RFQ with an Agent Code, or use the irrigation RFQ and fertigation RFQ for the water and dosing packages.