INDUSTRY ARCHITECTURE / Agriculture & food

Agriculture & food architecture

A connected reference design spanning domain information, data engineering, AI services, agent execution, and governance.

DOMAIN DESIGN DECISION

The business context
shapes the architecture.

A field observation depends on location, crop stage, and collection conditions; a supply record depends on batch lineage and handoffs.

Retain location precision, observation time, units, crop stage, and batch relationships. Do not treat an image inference as a verified field finding.

Start with field observation review rather than attempting to automate the whole industry at once. Define a bounded request, the evidence required to answer it, and the person accountable for accepting the result.

DATA → AI → AGENTIC AIDOMAIN CONTEXT: Field / Observation / Batch / Inspection record

Data architecture

01
Domain sources
Field observations, weather feeds, stock records, inspection reports. Identify the system of record, owner, refresh rate, and approved purpose for each source.
02
Ingestion & contracts
Batch, change-data capture, events, and document intake as appropriate. Validate schema, identity keys, event time, and rejected records.
03
Storage & transformation
Separate raw evidence, validated records, and curated domain products. Preserve version history and reconciliation results.
04
Semantic serving
Field · Observation · Batch · Inspection record. Publish agreed definitions, access rules, and reusable interfaces for analytics and AI.

AI architecture

01
Approved information
Use curated domain products and approved documents. Preserve metadata, effective dates, and access scope.
02
Retrieval & features
Build permission-aware retrieval indexes or model features for the specific task. Test relevance and freshness independently.
03
Model & evaluation
Evaluate candidate methods on representative domain examples. Check groundedness, completeness, uncertainty, cost, and failure behavior.
04
Experience & feedback
Serve sourced drafts or scored signals through a review experience. Capture feedback without silently retraining or widening data access.

Agentic AI architecture

01
Task & identity
Record the initiating user, allowed purpose, task boundary, and completion criteria.
02
Reasoning & state
Field observation agent and Supply traceability agent. Keep task state, retrieved evidence, and unresolved questions traceable.
03
Scoped enterprise tools
Expose typed, permission-checked search, lookup, and draft tools. Validate arguments and handle retry or timeout safely.
04
Approval & audit
Agronomic treatment, food-safety determinations, and supplier actions require qualified review. Record tool calls, reviewer decisions, and the accepted result under an agreed retention policy.

Shared governance & operationsOwnership · classification · lineage · access · quality · retention · evaluation · observability · incident response · cost management

Implementation decisions
to resolve before build.

Data lifecycle

Identify authoritative records, history requirements, update frequency, retention, and the consequences of stale information. Preserve source event time and ingestion time where ordering matters.

Serving & integration

Choose batch, event, API, or document access based on workload needs. Separate read-only retrieval from tools that alter enterprise records. Define timeouts, retries, and recovery ownership.

Operational evidence

Measure record completeness, batch lineage resolution, observation consistency, and specialist escalation quality. Keep versioned evaluation cases, end-to-end traces, and reviewer feedback so changes can be assessed against an agreed baseline.

CASE

A field observation and batch evidence workspace

See the proposed architecture applied to a focused workflow.

Field & supply intelligence

Connect field observations, planning data, and food supply records.