Continuous Batching & Power-Aware Request Router with OpenAI Agents SDK and Claude 4.1 Opus
Construct a continuous batching and power-aware request router using OpenAI Agents SDK and Claude 4.1 Opus to schedule long-context LLM workloads across heterogeneous nodes. Achieve 25% energy savings without violating p99 latency SLAs during peak traffic simulations.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an intelligent scheduler with OpenAI Agents SDK and Claude 4.1 Opus to optimize continuous batching based on host thermal/power bounds.
How work is evaluated
Evaluates power savings percentage and p99 SLA compliance under synthetic serving traffic.
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.
Zero SLA Violations
Ensure no requests breach the defined p99 latency SLA.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Energy Savings Percent
Percentage reduction in energy usage compared to baseline unconstrained routing. • target: 0.28 • range: 0.25-0.5
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 a power-aware scheduler using OpenAI Agents SDK.
Leverage Claude 4.1 Opus for reasoning over complex thermal/power allocation policies.
Implement continuous batching queue managers that adjust batch size dynamically.
Reduce system energy draw while honoring strict SLA bounds.
Reference links and supporting material
Traffic trace logs and NVML power reading traces from 2,000 real-world LLM workloads.
Challenge setup
Evaluates power savings percentage and p99 SLA compliance under synthetic serving traffic.
Challenge input
JSON array of incoming requests with target SLA limits
OpenAI Agents SDK
Agent framework providing agent orchestration and tool execution
Claude 4.1 Opus
Frontier model used for deep policy evaluation and dynamic scheduling decisions
Evaluated output
JSON with total_energy_kwh, energy_savings_percent, and p99_latency_ms
- Ensure no requests breach the defined p99 latency SLA.
- Percentage reduction in energy usage compared to baseline unconstrained routing. • target: 0.28 • range: 0....
- Benchmark: Energy-Aware Inference Scheduling Benchmark
- Energy Savings Percent target: 0.28
- 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
- OpenAI · Required
- Claude Code · Optional
- Claude Code · Optional
- Claude 4.1 Opus · Optional
Evaluation contract
- Zero SLA Violations · Weight 1
- Energy Savings Percent · Weight 1
Metrics
- Energy Savings Percent · Target 0.28
Benchmarks
- Energy-Aware Inference Scheduling Benchmark
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON array of incoming requests with target SLA limits
Output: JSON with total_energy_kwh, energy_savings_percent, and p99_latency_ms
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
- Claude 4.1 Opus · Strategic Reasoning Engine
Recipe freeze marker
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