The data plumbing problem in agtech (fragmented sensors, siloed platforms, WhatsApp logistics threads) is really a systems architecture problem in disguise.
When data can't flow, decisions stall. When decisions stall, operators fill the gap with judgment calls that can't be audited, replicated, or improved over time.
The intelligence layer in any serious applied AI system has to start with data architecture — not model selection, not prompt engineering, not features.
What does that actually look like?
→ A single operational context that persists across sessions
→ Structured handoffs between data sources, not manual reconciliation
→ Task orchestration built on what the system knows, not what it has to ask
The farms and operations getting real AI leverage aren't using smarter models. They're running cleaner systems.
Structure first. Intelligence follows.