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.
Design a multi-agent system architecture using Vellum Canvas for a 'FinReg Sentinel' that performs regulatory monitoring. Define the roles of each agent (e.g., 'Document Analyst', 'Risk Assessor', 'Report Generator'), their communication protocols, and the overall workflow including steps for data ingestion, processing, analysis, and output generation. Specify how Durable will be integrated for persistent context.
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.
FinReg Sentinel: AI Agent for Crypto Compliance
The rapid expansion of crypto and fintech companies, necessitates robust, real-time regulatory compliance. This challenge tasks developers with building an advanced AI agent system that autonomously monitors and analyzes regulatory changes, assesses their impact on a hypothetical crypto enterprise, and generates actionable compliance reports. The system must process complex legal documents and financial news, identifying risks and suggesting policy adjustments. Leveraging modern generative AI, the agent will go beyond simple keyword matching, using large language models to understand the nuances of legal texts and synthesize coherent compliance recommendations. The focus is on creating an intelligent workflow that can adapt to evolving regulatory landscapes, providing proactive insights rather than reactive responses. This involves intricate prompt engineering, stateful agent design, and robust evaluation to ensure accuracy and relevance.
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.