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.
Develop the final synthesis mechanism where agents combine their findings into a cohesive, structured report. How will the A2A protocol facilitate the exchange of partial results, and how will a 'Chief Editor' agent (or similar) leverage GPT-5 to integrate these findings into a high-quality, comprehensive document?
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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.
A2A Protocol Multi-Agent System for Public Safety Tech Market Analysis with OpenAI Swarm and GPT-5
The surge in VC investment into public safety tech demands comprehensive analysis, including market trends, ethical implications, and potential societal impact. This challenge requires building an A2A protocol multi-agent system using OpenAI Swarm to collaboratively research and synthesize insights. Specialized agents (e.g., Market Analyst, Ethics Researcher, Policy Advisor) will communicate via A2A, leveraging GPT-5 for advanced reasoning and content generation. The system will integrate MCP-enabled tools for accessing market databases and legal/ethical frameworks, utilizing extended thinking with adaptive reasoning budgets to navigate complex and sensitive topics.
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.