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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Provide your agent with the following scenario: 'A fintech company based in London is launching a new investment product in India, requiring them to collect and store KYC (Know Your Customer) data locally. They are considering using a cloud provider with servers exclusively in the US, but want to ensure compliance with both UK data protection laws (e.g., UK GDPR) and Indian data residency requirements.' Instruct the agent to identify potential regulatory conflicts and propose high-level mitigation strategies, using its tools as needed.
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 rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
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 an AI-Powered Regulatory Compliance Risk Agent
Modern enterprises face complex legal and regulatory landscapes, particularly concerning data sovereignty and privacy. This challenge involves developing an autonomous agent designed to assess potential compliance risks for a multinational corporation, specifically focusing on data storage regulations, cross-border data transfer policies, and the implications of governmental legal orders on encrypted data. The agent will leverage advanced reasoning capabilities to interpret legal texts, identify potential vulnerabilities, and recommend mitigation strategies. The solution will utilize the OpenAI Agents SDK to orchestrate tool use, manage conversational state, and enable the agent to interact with a simulated legal database and a policy evaluation framework. The agent should be capable of understanding nuanced legal language and providing actionable insights for legal and compliance teams.
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.