implementation

Agent Orchestrator Setup

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Creative Integrity Orchestrator

Format

Code-aware

Lines

Sections

Linked challenge

Creative Integrity Orchestrator

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Using the OpenAI Agents SDK, initialize a main orchestrator agent that routes tasks. Example: from openai_agents import Agent; orchestrator = Agent(name='Manager', model='gpt-5.4-mini'). Create a sub-agent named 'LegalAnalyst' that uses DeepSeek R1 to evaluate script requests against WGA 2026 AI guidelines.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

Variables

Lists

Code blocks

Purpose

implementation

This prompt already mixes executable detail with instructions, so tune examples and interfaces before rewriting the scaffold.

Linked challenge

Creative Integrity Orchestrator

In response to the WGA and AMPTP agreement regarding AI training protection and creative workflows, you will build an autonomous agent team that automates script review and IP protection. Using the OpenAI Agents SDK, you will orchestrate a multi-agent system where one agent specializes in deep reasoning about copyright law and union agreements, while another manages creative content generation. The system must verify that any generated content adheres to the 2026 labor agreements and does not inadvertently ingest restricted training data. You will implement a governance layer using Fairlearn to ensure the creative output is free from specific demographic biases that often plague large models. The workflow will be optimized by Yupp AI, which routes reasoning tasks to DeepSeek R1 for logic-heavy analysis and creative tasks to GPT-5.4-mini for rapid iteration. Finally, you will integrate Cognition (Devin) as an autonomous engineering agent to perform code reviews on the system's own tool-calling logic to prevent data leaks.

Open challenge

Related prompts

Browse library

Initial Assessment Prompt

planning

Content Classification Prompt

implementation

Problem Definition and Requirements Gathering

To establish a clear understanding of the problem space and define comprehensive requirements for the AI solution.