AutoGen Multi-Agent System for Media Strategic Resource Planning
Modern media companies grapple with challenging financial priorities, resource allocation, and strategic content decisions. This challenge focuses on building an AutoGen-powered multi-agent system designed to assist a media executive in making critical decisions, such as allocating reporting resources for major events (e.g., the Winter Olympics) or evaluating investment in new content verticals, based on financial data and projected audience impact. The system will feature several AutoGen agents (e.g., 'Finance Analyst', 'Content Strategist', 'Audience Insights Specialist') that engage in a collaborative conversation to analyze scenarios, debate pros and cons, and ultimately present a reasoned recommendation. The agents will have access to simulated financial data and audience engagement metrics, using OpenAI o3 as their conversational backbone.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an AutoGen multi-agent system to facilitate strategic planning and resource allocation for media companies using OpenAI o3.
How work is evaluated
The AutoGen system will be evaluated on the quality and coherence of its strategic recommendations, the effectiveness of agent collaboration, and the appropriate use of tools and data.
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.
Recommendation Format Adherence
Generated recommendation adheres to the specified JSON structure.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Tool Usage During Discussion
Agents successfully invoke relevant tools during the strategic discussion.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Recommendation Coherence
Logical consistency and clarity of the recommended action and justification. • target: 4 • range: 1-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Data Integration Accuracy
Degree to which agents accurately refer to or incorporate provided financial and audience data. • target: 85 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Collaborative Problem Solving
Assessment of how well agents explored different facets of the problem and addressed counter-arguments. • target: 4 • 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 UserProxyAgent, AssistantAgent, and GroupChat for building flexible multi-agent conversational workflows.
Implement agents with distinct roles such as 'Finance Analyst', 'Content Strategist', and 'Audience Insights Specialist', each with specific prompts and capabilities.
Design custom Python tools for agents to query and analyze simulated financial statements and audience engagement data stored in a lightweight SQLite database.
Utilize OpenAI o3 (e.g., gpt-3.5-turbo or a similar 'o3' class model) as the conversational model for agent interactions and reasoning.
Develop a 'Reporting Tool' function that agents can use to compile their findings and recommendations into a structured output format (e.g., JSON or Markdown).
Orchestrate a strategic planning session where agents collaboratively discuss a given business challenge, propose solutions, and reach a consensus or highlight trade-offs.
Incorporate human-in-the-loop interaction with the UserProxyAgent for iterative refinement and oversight of the strategic planning process.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The AutoGen system will be evaluated on the quality and coherence of its strategic recommendations, the effectiveness of agent collaboration, and the appropriate use of tools and data.
Challenge input
{'scenario': 'string', 'financial_data_snapshot': 'dict', 'audience_metrics_snapshot': 'dict'}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'decision_area': 'string', 'recommended_action': 'string', 'justification': 'string', 'pros': ['string'], 'cons': ['string'], 'metrics_considered'...
- Generated recommendation adheres to the specified JSON structure.
- Agents successfully invoke relevant tools during the strategic discussion.
- Logical consistency and clarity of the recommended action and justification. • target: 4 • range: 1-5
- Recommendation Coherence 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
- Recommendation Format Adherence · Weight 1
- Tool Usage During Discussion · Weight 1
- Recommendation Coherence · Weight 1
- Data Integration Accuracy · Weight 1
- Collaborative Problem Solving · Weight 1
Metrics
- Recommendation Coherence · Target 4
- Data Integration Accuracy · Target 85
- Collaborative Problem Solving · Target 4
Reference cases
- 2 public reference cases
Workflow tasks
- Task 1
Input: {'scenario': 'string', 'financial_data_snapshot': 'dict', 'audience_metrics_snapshot': 'dict'}
Output: {'decision_area': 'string', 'recommended_action': 'string', 'justification': 'string', 'pros': ['string'], 'cons': ['string'], 'metrics_considered': ['string']}
Execution harness: Python
- Task 2
Input: {'discussion_topic': 'string'}
Output: {'agent_dialogue_summary': 'string', 'key_points_discussed': ['string'], 'tool_usage_log': ['string']}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.