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.
Run your complete LangGraph-based agent system on the provided `CreditScoreImprovementPlan` and `FinancialDataAnalysis` test cases with diverse simulated user profiles. Evaluate the quality of the generated advice, the accuracy of financial analysis, and the smooth execution of the agent workflow. Detail any refinements made to the workflow or agent prompts based on testing.
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.
FinTech Credit Advisor
The intersection of FinTech and AI offers immense opportunities for personalized financial guidance. This challenge involves building an intelligent credit building advisor using a graph-based multi-agent system. The advisor will leverage Gemini 3 Pro's multimodal capabilities and hybrid reasoning to analyze a user's simulated financial data. The goal is to provide actionable, personalized advice for improving credit scores. The system will be orchestrated using LangGraph to define dynamic, stateful financial advisory workflows. Agents will communicate using an A2A protocol (simulated) for seamless data exchange and decision-making. Key features include MCP tool integration to connect with simulated financial data APIs (bank statements), adaptive reasoning budgets for complex financial scenarios, and the generation of tailored credit improvement plans.
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.