OpenAI Agents SDK Hardware Telemetry & Energy Attribution System
Multi-tenant cloud infrastructure often fails to accurately attribute energy consumption and accelerator costs to individual model workloads. Build an OpenAI Agents SDK system that parses Prometheus and OpenTelemetry metrics to allocate energy (Joules), power (Watts), and cost ($) down to specific tenant requests.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an OpenAI Agents SDK telemetry pipeline that correlates token generation with GPU energy usage and tenant cost attribution.
How work is evaluated
Tests tenant energy attribution accuracy given overlapping multi-tenant inference workloads.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
energy_balance_check
Checks that total attributed energy equals total energy input.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
attribution_precision
Fidelity of tenant cost and power attribution • target: 0.99 • range: 0.95-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Use OpenAI Agents SDK function calling to ingest OpenTelemetry hardware metrics
Attribute GPU power draw (via NVML/Prometheus) to specific tenant request sessions
Calculate energy usage per 1k prompt and generation tokens
Generate tenant billback reports detailing compute time, energy, and financial cost
Reference links and supporting material
Prometheus metric snapshots containing NVML energy counter streams and API gateway request logs.
How this agent runs
Tests tenant energy attribution accuracy given overlapping multi-tenant inference workloads.
Challenge input
JSON containing total_energy_joules, active_tenants, tenant_token_logs
OpenAI Agents SDK
OpenAI function calling and agent orchestration SDK
Evaluated output
JSON containing tenant_attribution_joules, tenant_cost_usd, unassigned_overhead_pct
- Checks that total attributed energy equals total energy input.
- Fidelity of tenant cost and power attribution • target: 0.99 • range: 0.95-1
- Benchmark: Cloud FinOps Cost Attribution Standard
- Attribution Precision target: 0.99
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- OpenAI · Required
- Anthropic · Optional
- Azure OpenAI Service · Optional
Evaluation contract
- energy_balance_check · Weight 1
- attribution_precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.