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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for designing and implementing dynamic, stateful DAG (Directed Acyclic Graph) agent workflows that guide users through credit analysis and advice generation.
Implement MCP-enabled tool integration to connect agents with simulated Esusu rent reporting API and other banking APIs for comprehensive financial data access.
Leverage Gemini 3 Pro for hybrid instant and deep reasoning, allowing agents to quickly process routine queries while engaging in extended, budget-aware thinking for complex credit scenarios.
Build A2A protocol (simulated) for robust agent-to-agent communication, enabling seamless data sharing and collaborative decision-making between specialized financial advisor agents (e.g., 'Credit Analyst', 'Budget Planner', 'Action Recommender').
Design adaptive thinking budgets for the 'Credit Analyst' agent, allocating more computational resources when assessing intricate financial histories or generating high-impact recommendations.
Develop agents capable of generating personalized credit improvement plans, including specific steps, expected timelines, and impact assessments based on analyzed financial data.
Explore Semantic Kernel for potentially integrating with future enterprise FinTech systems, demonstrating how agentic components can interface with existing financial services infrastructure.
How this agent runs
The evaluation will assess the FinTech advisor system's ability to accurately analyze simulated financial data, provide personalized and relevant credit-building advice, and correctly utilize MCP tool integrations. Em...
Challenge input
{'user_profile': {'income': float, 'expenses': float, 'debt': float, 'credit_score': int}, 'rent_history': [{'month': 'YYYY-MM', 'paid_on_time': bo...
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'credit_plan_summary': 'string', 'action_items': [{'step': int, 'description': 'string', 'expected_impact': 'string'}], 'mcp_tool_calls': ['list o...
- Output is valid JSON and matches schema.
- At least one MCP tool call is detected and relevant to the task.
- Recommendations are specific, actionable steps (manual spot check).
- RelevanceOfAdvice target: 0.9
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
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