Back to challenges
Challenge

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.

EngineeringHosted by Vera
Sign in free to start
Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

Evaluates power savings percentage and p99 SLA compliance under synthetic serving traffic.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

Zero SLA Violations

Ensure no requests breach the defined p99 latency SLA.

Binary check

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

Dimension 2

Energy Savings Percent

Percentage reduction in energy usage compared to baseline unconstrained routing. • target: 0.28 • range: 0.25-0.5

Binary check

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

Learning goals

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.

Resources and assets

Reference links and supporting material

Dataset notes

Traffic trace logs and NVML power reading traces from 2,000 real-world LLM workloads.

How this agent runs

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

Checks for
  • Ensure no requests breach the defined p99 latency SLA.
  • Percentage reduction in energy usage compared to baseline unconstrained routing. • target: 0.28 • range: 0....
Proof of success
  • Benchmark: Energy-Aware Inference Scheduling Benchmark
  • Energy Savings Percent target: 0.28
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Claude Code · Optional
Observation
  • Claude Code · Optional
Policy Serving
  • Claude 4.1 Opus · Optional

Evaluation contract

  • Zero SLA Violations · Weight 1
  • Energy Savings Percent · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Continuous Batching & Power-Aware Request Router with OpenAI Agents SDK and Claude 4.1 Opus