Waste-to-Energy Dispatch Agent with OpenAI Agents SDK
Municipal waste facility operators need automated load balancing to maximize energy output while remaining compliant with environmental emissions thresholds. Build an autonomous waste-to-energy dispatch agent using the OpenAI Agents SDK routed through Vercel AI Gateway to maintain combustion zone stability.
What you are building
The core problem, expected build, and operating context for this challenge.
Design an autonomous dispatch agent using the OpenAI Agents SDK and tool functions to regulate waste feedstock feed rates.
How work is evaluated
Evaluates agent capability to adjust waste feeding rates based on caloric telemetry without exceeding emissions caps.
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.
temperature_stability_check
Ensure combustion temperature remains between 850C and 1050C.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
feed_rate_accuracy
Absolute error between calculated optimal feed rate and agent recommendation. • 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 autonomous tool invocation using OpenAI Agents SDK
Route agent model traffic through Vercel AI Gateway for resilience and observability
Parse real-time waste sensor telemetry and calculate optimal furnace feed rates
Enforce environmental compliance boundaries within dynamic tool function logic
Reference links and supporting material
Simulated telemetry dataset containing waste caloric values, moisture content, furnace temperatures, and target emissions metrics for waste-to-energy plants.
Challenge setup
Evaluates agent capability to adjust waste feeding rates based on caloric telemetry without exceeding emissions caps.
Challenge input
JSON object with caloric_value_kj_kg, moisture_content_pct, and current_temp_c
OpenAI Agents SDK
Core framework required for agent orchestrations.
Vercel AI Gateway
Provides proxy routing and monitoring for LLM API calls.
Evaluated output
JSON object with recommended_feed_rate_tph and airflow_ratio
- Ensure combustion temperature remains between 850C and 1050C.
- Absolute error between calculated optimal feed rate and agent recommendation. • target: 0.95 • range: 0-1
- Benchmark: EnergyOps-Eval-v1
- Feed Rate Accuracy 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.
View technical recipe
Configured tools
- OpenAI · Required
- VAPI · Optional
- Vercel AI Gateway · Optional
- Vercel AI Gateway · Optional
Evaluation contract
- temperature_stability_check · Weight 1
- feed_rate_accuracy · Weight 1
Metrics
- feed_rate_accuracy · Target 0.95
Benchmarks
- EnergyOps-Eval-v1
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with caloric_value_kj_kg, moisture_content_pct, and current_temp_c
Output: JSON object with recommended_feed_rate_tph and airflow_ratio
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · agent_framework
- Vercel AI Gateway · llm_gateway
Recipe freeze marker
No recipe freeze time is recorded.