Agent for Auditable Financial Model Generation
To make financial modeling predictable and auditable, this challenge focuses on building an AI agent system using LlamaIndex for advanced financial analysis. Unlike traditional multi-agent tool-calling applications, this challenge emphasizes LlamaIndex's agentic capabilities for structured data processing, tool use, and complex reasoning without relying on tool orchestrations. Participants will design an agent that can ingest raw financial data (e.g., CSV, JSON), apply business logic, generate financial models, and produce comprehensive audit trails. The system will use GPT-5 for core reasoning and model generation, with Claude Sonnet 5 for summarization and clarification. Ray Serve and Novita AI will be leveraged for efficient and scalable deployment of these models, ensuring reliable inference. The agent will interact with simulated financial APIs and spreadsheet tools, producing auditable outputs that explain its reasoning and data transformations, enhancing trust and transparency in AI-driven financial insights.
What you are building
The core problem, expected build, and operating context for this challenge.
To make financial modeling predictable and auditable, this challenge focuses on building an AI agent system using LlamaIndex for advanced financial analysis. Unlike traditional multi-agent tool-calling applications, this challenge emphasizes LlamaIndex's agentic capabilities for structured data processing, tool use, and complex reasoning without relying on tool orchestrations. Participants will design an agent that can ingest raw financial data (e.g., CSV, JSON), apply business logic, generate financial models, and produce comprehensive audit trails. The system will use GPT-5 for core reasoning and model generation, with Claude Sonnet 5 for summarization and clarification. Ray Serve and Novita AI will be leveraged for efficient and scalable deployment of these models, ensuring reliable inference. The agent will interact with simulated financial APIs and spreadsheet tools, producing auditable outputs that explain its reasoning and data transformations, enhancing trust and transparency in AI-driven financial insights.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Correct Model Calculation
The generated financial projections must be numerically accurate based on input data and parameters.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Audit Trail Presence
The 'audit_trail' list must contain at least 3 distinct steps.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Audit Trail Detail
The average length of individual entries in the 'audit_trail' (in characters). • target: 50 • range: 20-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master LlamaIndex's agent framework for building complex, tool-calling agents that interact with structured data sources (e.g., CSV, JSON).
Design and implement custom tools for LlamaIndex agents to perform financial calculations, interact with simulated spreadsheet APIs, and retrieve market data.
Integrate GPT-5 as the primary reasoning engine for the LlamaIndex agent, enabling complex financial planning, scenario analysis, and model generation.
Utilize Claude Sonnet 4 within the LlamaIndex agent for generating clear, concise summaries of financial reports and explaining modeling assumptions or audit trails.
Deploy LlamaIndex agents and their underlying GPT-5 and Claude Sonnet 4 models using Ray Serve for scalable and fault-tolerant inference serving.
Leverage Novita AI for optimizing the deployment and runtime performance of the AI models, ensuring high throughput for financial analysis tasks.
Develop strategies for generating comprehensive audit trails within the LlamaIndex agent's workflow, detailing data transformations, model choices, and decision-making steps.
Implement validation checks within the LlamaIndex agent to ensure the predictability and accuracy of generated financial models.
How this agent runs
Evaluation will assess the LlamaIndex agent's accuracy in financial modeling, the completeness and clarity of its audit trails, and its ability to handle structured financial data.
Challenge input
{'financial_data_csv': 'string (CSV content)', 'model_parameters': 'dict'}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'financial_model_output': 'dict', 'audit_trail': 'list of strings', 'summary': 'string'}
- The generated financial projections must be numerically accurate based on input data and parameters.
- The 'audit_trail' list must contain at least 3 distinct steps.
- The average length of individual entries in the 'audit_trail' (in characters). • target: 50 • range: 20-100
- Audit Trail Detail target: 50
- 1 public reference case
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Correct Model Calculation · Weight 1
- Audit Trail Presence · Weight 1
- Audit Trail Detail · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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