Waste-to-Energy Plant Dispatch Optimizer with Pydantic AI
Casablanca's waste-to-energy facility requires continuous optimization of municipal waste input streams and thermal output. Build a type-safe dispatch optimization agent using Pydantic AI and Vercel AI Gateway to balance energy production and emissions limits. The solution must achieve 95% compliance on regulatory constraints.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement a type-safe dispatch agent using Pydantic AI to enforce schema-validated telemetry processing and operational bounds.
How work is evaluated
Evaluates Pydantic AI dispatch model outputs against environmental limits and target output metrics.
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
Output strictly adheres to DispatchPlan Pydantic model
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
emissions_compliance_rate
Percentage of generated setpoints adhering to environmental caps • target: 0.95 • 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
Implement structured type validation using Pydantic AI models for operational parameters
Integrate Vercel AI Gateway for resilient route orchestration and fallback handling
Optimize waste mixture ratios to satisfy temperature and stack emission constraints
Evaluate dispatch decisions against synthetic waste-to-energy sensor streams
Reference links and supporting material
Synthetic time-series telemetry representing waste feed volume, moisture levels, caloric value, and stack emission sensor data.
Challenge setup
Evaluates Pydantic AI dispatch model outputs against environmental limits and target output metrics.
Challenge input
JSON object with hourly waste feed rates and caloric values
Pydantic AI
Provides type-safe structured data output guarantees for critical industrial automation.
Vercel AI Gateway
Ensures unified provider fallbacks and low-latency API call routing.
Evaluated output
JSON object containing boiler setpoints and predicted emissions
- Output strictly adheres to DispatchPlan Pydantic model
- Percentage of generated setpoints adhering to environmental caps • target: 0.95 • range: 0-1
- Benchmark: EnergyAgentBench-2026
- Emissions Compliance Rate target: 95%
- 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.
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Configured tools
- Pydantic AI · Required
- Voiceflow · Optional
- Vercel AI Gateway · Optional
- Pydantic AI · Required
- Vercel AI Gateway · Optional
Evaluation contract
- schema_validation_pass · Weight 1
- emissions_compliance_rate · Weight 1
Metrics
- emissions_compliance_rate · Target 0.95
Benchmarks
- EnergyAgentBench-2026
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with hourly waste feed rates and caloric values
Output: JSON object containing boiler setpoints and predicted emissions
Execution harness: Python
Evaluation tools
- Pydantic AI · Agent Framework
- Vercel AI Gateway · Model Gateway
Recipe freeze marker
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