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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Initiate a strategic planning session where the `UserProxyAgent` (executive) asks the agents to discuss and provide recommendations on the following challenge: 'Given the declining demand for ruble-backed stablecoins (like A7A5) and the rise of other crypto assets, should our media outlet pivot its crypto coverage strategy? If so, what specific areas should we focus on to maximize audience engagement and ad revenue?' Ensure agents use their tools to retrieve relevant (mock) data during the discussion and ultimately use the `reporting_tool` to present a final recommendation.
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 rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
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.