The operating problem
An operations team organizes field observations and food-supply inspection records to identify missing context before specialist review. The workflow is not simply a question-answering task: it requires a reliable relationship between business entities, current evidence, and the review responsibilities of staff.
A field observation depends on location, crop stage, and collection conditions; a supply record depends on batch lineage and handoffs. A useful initial scope therefore focuses on assembling the right context and making missing information visible. It avoids treating a generated answer as an approved business decision.
The proposed data foundation
Potential inputs include field observations, weather feeds, stock records, inspection reports. Establish a source inventory before choosing connectors. For each input, record its owner, approved use, identifier conventions, update cadence, and retention requirements.
Retain location precision, observation time, units, crop stage, and batch relationships. Do not treat an image inference as a verified field finding. The curated domain model centers on Field, Observation, Batch, Inspection record. Keep raw source evidence separate from validated records and from the outputs prepared for a reviewer. This separation makes reconciliation and incident investigation more manageable.
AI and agent responsibilities
Organize observations and flag changes for agronomist review. A permission-aware retrieval layer can assemble the relevant approved context. Extraction or generation should return structured drafts, source references, and unresolved questions; it should not silently supply facts that are absent from the records.
Assemble batch records and identify missing traceability evidence. A second bounded responsibility can handle a related task while retaining the same identity and governance boundary. Multi-agent coordination is optional: use it only when separated responsibilities improve traceability or control.
Human review and exception paths
Agronomic treatment, food-safety determinations, and supplier actions require qualified review. Missing documents, conflicting records, unavailable tools, and low-quality retrieval should lead to a visible exception. Define who can resolve each exception and what evidence must be retained with the decision.
How the scenario would be evaluated
Proposed evaluation areas: record completeness, batch lineage resolution, observation consistency, and specialist escalation quality. Build a representative set containing normal cases, incomplete cases, outdated information, access restrictions, and tool failures. Compare the assisted workflow with the current process using the same acceptance criteria.
No percentage improvement, cost saving, or production result is asserted here. Any future benefit estimate should follow a measured baseline and a reviewed pilot, including the effort required to correct outputs and handle exceptions.
A focused next step
Agree one workflow, one accountable owner, a limited source set, and a review protocol. Use the first prototype to identify data gaps and operational changes, then decide whether additional integrations are justified.