Automated Content Moderation Workflow with OpenAI Agents SDK
In response to concerns about AI moderation errors, develop a robust multi-agent team that analyzes user content submissions. Utilizing the OpenAI Agents SDK, you will orchestrate a specialist team where one agent acts as a content screener, another performs sentiment validation, and the final agent produces human-readable explanations. This system uses Hume AI to detect emotional context in audio-based social content, ensuring moderation decisions are nuanced and auditable.
What you are building
The core problem, expected build, and operating context for this challenge.
In response to concerns about AI moderation errors, develop a robust multi-agent team that analyzes user content submissions. Utilizing the OpenAI Agents SDK, you will orchestrate a specialist team where one agent acts as a content screener, another performs sentiment validation, and the final agent produces human-readable explanations. This system uses Hume AI to detect emotional context in audio-based social content, ensuring moderation decisions are nuanced and auditable.
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.
ModerationLogicTest
Check if agent adheres to safety guidelines
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ModerationThroughput
Requests per minute • target: 50 • range: 10-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master the OpenAI Agents SDK pattern for defining multi-turn autonomous teams
Integrate Hume AI for context-aware moderation of social audio inputs
Automate documentation and log auditing for moderation events using Aider
Design agentic workflows in Twin to connect moderation tools with enterprise APIs
Evaluate moderation accuracy by implementing a human-in-the-loop verification step
Scale agent decision making using GPT-5.4-mini for efficient reasoning under load
How this agent runs
Moderation accuracy audit
Challenge input
Voice recording clip
OpenAI
OpenAI AI model provider
Hume AI
Emotionally intelligent voice AI
Twin
AI automation platform
Evaluated output
Boolean flag and explanation
- Check if agent adheres to safety guidelines
- Requests per minute • target: 50 • range: 10-100
- ModerationThroughput target: 50
- 1 public reference case
- JavaScript execution harness
View technical recipe
Configured tools
- OpenAI · Required
- Hume AI · Optional
- Twin · Optional
Evaluation contract
- ModerationLogicTest · Weight 1
- ModerationThroughput · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
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