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Challenge

Enterprise Robotaxi Lobbying Agent with Claude Agents SDK

Build an intelligent lobbying and policy simulation agent using the Claude Agents SDK. This system is designed to simulate the competitive landscape between Uber and Waymo regarding AV legislation. You will deploy GPT-5.4 Pro to generate adversarial lobbying arguments while utilizing Claude Sonnet 4.6.6 as the policy-evaluator that checks alignment with proposed regulatory frameworks. This challenge uses Fireworks AI for high-throughput inference and Triton Inference Server for model runtime deployment. The goal is to build an autonomous agent that drafts policy briefs and simulates the impact of lobbying efforts based on current legislative headlines.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a legislative strategy agent using Claude Agents SDK, GPT-5.4 Pro, and Fireworks AI for inference.

Delivery guide

How work is evaluated

Evaluation

Assess the agent's capability to draft legally coherent policy briefs.

Datasets

Challenge datasets

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

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

Argument Coherence

Check logical consistency of the policy argument

Binary check

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

Dimension 2

Inference Latency

Ensure under 200ms per request via Triton • target: 150 • range: 0-500

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

  • Master Claude Agents SDK for building agents with advanced tool-calling and computer use

  • Implement multi-model orchestration with GPT-5.4 Pro and Claude Sonnet 4.6.6

  • Deploy scalable model runtimes using Triton Inference Server

  • Optimize inference performance using Fireworks AI API

  • Design legislative policy simulation workflows that account for multi-stakeholder logic

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

Assess the agent's capability to draft legally coherent policy briefs.

Challenge input

Position paper premise

Triton Inference Server

Production model server by NVIDIA.

Fireworks AI

Fast inference and fine-tuning platform.

GPT-5

Policy Serving in the agent workflow.

Evaluated output

Policy brief document

Evaluation checks
  • Check logical consistency of the policy argument
  • Ensure under 200ms per request via Triton • target: 150 • range: 0-500
Configured evaluation evidence
  • Inference Latency target: 150
  • 1 public reference case
Configured runtime
  • Docker execution harness

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

Action Space
  • Triton Inference Server · Required
Policy Serving
  • Fireworks AI · Optional
  • GPT-5 · Optional
Training Infra
  • Fireworks AI · Optional

Evaluation contract

  • Argument Coherence · Weight 1
  • Inference Latency · Weight 1

Metrics

  • Inference Latency · Target 150

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: Position paper premise

    Output: Policy brief document

    Execution harness: Docker

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Enterprise Robotaxi Lobbying Agent with Claude Agents SDK