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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.

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What you are building

Implement a type-safe dispatch agent using Pydantic AI to enforce schema-validated telemetry processing and operational bounds.

How work is evaluated

Evaluation

Evaluates Pydantic AI dispatch model outputs against environmental limits and target output metrics.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

schema_validation_pass

Output strictly adheres to DispatchPlan Pydantic model

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

emissions_compliance_rate

Percentage of generated setpoints adhering to environmental caps • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • 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

Resources

Dataset notes

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

Evaluation checks
  • Output strictly adheres to DispatchPlan Pydantic model
  • Percentage of generated setpoints adhering to environmental caps • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: EnergyAgentBench-2026
  • Emissions Compliance Rate target: 95%
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Voiceflow · Optional
Policy Serving
  • Vercel AI Gateway · Optional
Orchestration
  • 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

  1. 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

No recipe freeze time is recorded.

Frequently Asked Questions about Waste-to-Energy Plant Dispatch Optimizer with Pydantic AI