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.
Design the overall architecture of the social media monitoring system. Define the specific roles and responsibilities for at least three distinct agents (e.g., 'Monitor Agent', 'Policy Interpretation Agent', 'Risk Assessment Agent'). Describe how these agents will interact within a LangGraph workflow, including the state transitions and data flow. Specify how MCP will be used for tool integration.
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.
Build MCP-Enabled Social Media Policy Enforcement Agents
Companies are increasing social media monitoring due to reputational risks from employee posts. This challenge involves building an advanced multi-agent system that autonomously monitors public social media feeds for potential employee policy violations. The system will leverage a graph-based workflow to analyze content, interpret company policies (retrieved via RAG), and flag potential issues, while adapting its reasoning budget based on the sensitivity of the content or the severity of the potential violation. You will design and implement a sophisticated LangGraph-based agent network where a 'Monitor Agent' feeds data to a 'Policy Interpretation Agent' and a 'Risk Assessment Agent'. These agents will communicate using a defined protocol, integrating with external enterprise policy databases via MCP for real-time policy lookup and dynamically adjusting their processing depth (thinking budget) to balance efficiency and accuracy. The system must provide actionable insights and prioritize alerts for human review, ensuring compliance while minimizing false positives.
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.