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.
Set up an AutoGen GroupChat with three agents: 'RegulatoryAnalyst', 'FinancialController', and 'ChiefEngineer'. Initialize them using pyautogen and configure the 'RegulatoryAnalyst' to specifically look for Supreme Court rulings mentioned in the ENR news URLs provided.
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.
Infrastructure Project Permitting Multi-Agent System with AutoGen and Lovable Dev
Large-scale infrastructure projects like the $11B Grain Belt Express or the $1B gas-fired power plant in West Virginia face complex regulatory and engineering hurdles. In this challenge, you will use Microsoft AutoGen to build a multi-agent system that simulates an infrastructure planning committee. One agent acts as a 'Regulatory Analyst' (citing Illinois Supreme Court backing), another as a 'Financial Controller' ($2B Genentech expansion context), and a third as an 'Engineering Consultant'. You will use AutoGen's conversational patterns to reach a consensus on project feasibility. To visualize these complex agent interactions and project statuses, you will use Lovable Dev to build a modern dashboard. This 'Infrastructure War Room' will display real-time logs from the AutoGen agents, permit statuses, and budget tracking for projects like the RFK Stadium proposal or Massachusetts' $8B transportation investment.
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.