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.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Define 3-5 distinct agent personas for an AutoGen multi-agent system (e.g., 'Industry Advocate', 'Privacy Rights Champion', 'Government Regulator'). Each persona should have specific goals and initial positions regarding national AI policy. Provide an initial, high-level policy brief that the agents must collectively refine.
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 role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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 Policy Consensus Agents with AutoGen & Claude Opus 4.1 for National AI Strategy
Drawing inspiration from the 'Pro-AI super PAC' advocating for a national AI policy, this challenge requires you to build an advanced multi-agent system capable of simulating complex legislative debate and drafting a consensus AI policy. Using AutoGen, you will orchestrate a team of specialized agents, each representing a different stakeholder (e.g., Pro-Innovation, Ethics Advocate, Legal Expert). The system will implement a secure A2A protocol for inter-agent communication, allowing for robust negotiation and information exchange. Crucially, agents will employ extended thinking with adaptive reasoning budgets using Claude Opus 4.1 to analyze legislative proposals, identify conflicts, and collaboratively draft a unified national AI policy that overrides a patchwork of state laws. Integration of MCP will allow agents to access and verify information from mock legislative databases.
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.