Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Initialize an AutoGen `GroupChat` with at least three agents: a `UserProxyAgent` (representing the executive), an `AssistantAgent` acting as a 'Finance Analyst', and another `AssistantAgent` as a 'Content Strategist'. Define their roles, system messages, and use OpenAI o3 as the LLM for all agents. Implement a custom Python function `query_financial_data(metric, quarter)` that simulates querying financial data from a simple SQLite database and register it as a tool for the 'Finance Analyst'.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the source structure until you know which part of the prompt is actually driving the result quality.
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
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.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.