Agri-DPI Crop Advisory Agent using Pydantic AI
Smallholder farmers require strict, type-safe agricultural advisory systems that parse soil and weather telemetry accurately. Build a structured AI agent using Pydantic AI that generates verified fertigation schedules from sensor streams, ensuring 100% schema validation pass rates across 1,000 synthetic farm profile runs.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a type-safe agricultural advisory agent utilizing Pydantic AI to turn soil IoT telemetry into compliant crop action plans.
How work is evaluated
Evaluates agronomic output accuracy and Pydantic schema validation success.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
schema_validation_pass
Ensures model output passes strict Pydantic parsing without errors.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
schema_pass_rate
Pass rate of structured schema outputs across batch test suite. • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build type-safe conversational and tool-calling agents using Pydantic AI
Enforce runtime validation on agricultural telemetry data inputs
Generate structured irrigation and fertilizer application schedules
Implement dependency injection for accessing weather external APIs within Pydantic AI agents
Reference links and supporting material
1,000 synthetic farm soil sensor profiles across diverse Indian agro-climatic zones.
Challenge setup
Evaluates agronomic output accuracy and Pydantic schema validation success.
Challenge input
JSON containing field_id, crop_type, moisture_percentage, nitrogen_level, and forecasted_rain_mm.
Pydantic AI
Guarantees runtime schema validation and dependency injection.
Evaluated output
JSON containing action ('IRRIGATE', 'APPLY_FERTILIZER', 'HOLD'), recommended_water_liters, and explanation.
- Ensures model output passes strict Pydantic parsing without errors.
- Pass rate of structured schema outputs across batch test suite. • target: 1 • range: 0-1
- Benchmark: Agritech Recommendation Validation Benchmark
- Schema Pass Rate target: 100%
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Pydantic AI · Required
- BoTorch · Optional
- Deepgram · Optional
- Pydantic AI · Required
Evaluation contract
- schema_validation_pass · Weight 1
- schema_pass_rate · Weight 1
Metrics
- schema_pass_rate · Target 1
Benchmarks
- Agritech Recommendation Validation Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing field_id, crop_type, moisture_percentage, nitrogen_level, and forecasted_rain_mm.
Output: JSON containing action ('IRRIGATE', 'APPLY_FERTILIZER', 'HOLD'), recommended_water_liters, and explanation.
Execution harness: Python
Evaluation tools
- Pydantic AI · Type-Safe Agent Engine
Recipe freeze marker
No recipe freeze time is recorded.