Multi-Agent AI for Dynamic Short-Form Video Generation
Design and implement a multi-agent system using AutoGen that simulates a creative studio for generating short-form video content (e.g., YouTube Shorts). Inspired by YouTube's vision for AI-generated media, this system will coordinate specialized agents like a 'Scriptwriter Agent,' 'Visuals Generator Agent,' and 'Editor Agent' to autonomously conceptualize, script, and outline visual elements for a short video based on a user's prompt. The challenge emphasizes complex multi-agent conversations, conditional workflows, and the integration of diverse AI models for different generation tasks, with human-in-the-loop validation at critical stages. This system should be capable of producing a detailed production plan and asset descriptions, even if it doesn't render the final video.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an AutoGen multi-agent system using Claude Opus 4.1 and OpenAI o3 for generating dynamic short-form video production plans and asset descriptions.
How work is evaluated
Evaluation will assess the generated production plan's completeness, creativity, adherence to the prompt, and feasibility of proposed visual assets.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
PlanCompleteness
All required components of the production plan (title, logline, script scenes, asset list) are present.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
PromptAdherence
Generated plan aligns with the user's initial prompt and constraints (audience, length, theme).
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
AgentCommunicationFlow
Agents engage in a logical and coherent conversation to reach the content generation goal.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
HumanFeedbackIntegration
Human feedback is correctly processed and results in appropriate, relevant plan revisions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Creativity_Score
Subjective score for the originality and entertainment value of the generated plan (1-5, 5 being highly creative). • target: 4 • range: 1-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
VisualFeasibility_Score
How realistic and detailed the visual descriptions are for actual video production (1-5, 5 being highly feasible). • target: 4 • range: 1-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Iteration_Efficiency
Number of turns required for agents to effectively address human feedback (fewer is better, target around 2-3 turns). • target: 2 • range: 1-5
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 AutoGen's ConversableAgent and UserProxyAgent to create complex multi-agent conversations.
Implement advanced agent roles using Claude Opus 4.1 for creative conceptualization and script generation.
Leverage OpenAI o3 for specialized tasks such as generating visual asset descriptions or catchy titles.
Design and integrate custom tools for agents to simulate external media generation services.
Implement dynamic workflow branching in AutoGen based on agent outputs or human feedback.
Explore how DeepSpeed can optimize fine-tuned models for specific content generation sub-tasks within the agent workflow.
Orchestrate output storage of generated plans and assets using Azure Blob Storage.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
Evaluation will assess the generated production plan's completeness, creativity, adherence to the prompt, and feasibility of proposed visual assets.
Challenge input
{"user_prompt": "str", "target_audience": "str", "video_length_seconds": "int"}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{"title": "str", "logline": "str", "script_scenes": [{"scene_number": "int", "dialogue": "str", "visuals": "str", "sound_cues": "str"}], "asset_lis...
- All required components of the production plan (title, logline, script scenes, asset list) are present.
- Generated plan aligns with the user's initial prompt and constraints (audience, length, theme).
- Agents engage in a logical and coherent conversation to reach the content generation goal.
- Creativity Score target: 4
- 2 public reference cases
- Python 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
No tool records are attached.
Evaluation contract
- PlanCompleteness · Weight 1
- PromptAdherence · Weight 1
- AgentCommunicationFlow · Weight 1
- HumanFeedbackIntegration · Weight 1
- Creativity_Score · Weight 1
- VisualFeasibility_Score · Weight 1
- Iteration_Efficiency · Weight 1
Metrics
- Creativity_Score · Target 4
- VisualFeasibility_Score · Target 4
- Iteration_Efficiency · Target 2
Reference cases
- 2 public reference cases
Workflow tasks
- Task 1
Input: {"user_prompt": "str", "target_audience": "str", "video_length_seconds": "int"}
Output: {"title": "str", "logline": "str", "script_scenes": [{"scene_number": "int", "dialogue": "str", "visuals": "str", "sound_cues": "str"}], "asset_list": ["str"], "feedback_points": ["str"]}
Execution harness: Python
- Task 2
Input: {"initial_plan": {"title": "str", "logline": "str", "script_scenes": [...]}, "human_feedback": "str"}
Output: {"revised_plan": {"title": "str", "logline": "str", "script_scenes": [...]}, "changes_made": "str"}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.