Building Reliable Data Analytics Agents: Lessons from the KDD Cup | NVIDIA Technical Blog

This post details the KGMON team's approach to building reliable data analytics agents for the KDD Cup 2026 competition. Key contributions include: 1. Unifying structured data sources (databases, CSV, JSON) into a single SQLite database with a narrow schema() and sql() interface to reduce routing errors and preserve reasoning capacity. 2. Implementing a preflight schema-scouting step to brief the agent on tables, join keys, and data-quality issues before the main reasoning loop. 3. Building a small, opinionated tool harness with functions like schema(), sql(query), write_answer(df), and prose_helper(), with middleware for repairing malformed calls and persistent state for intermediate results. 4. Separating document inspection from structured analysis by using targeted previews and regex searches fed to a prose_helper tool (with zero-temperature LLM calls) to extract answers or tables, keeping raw prose out of the main context. 5. Preprocessing video data by extracting keyframes, transcribing audio, and aligning segments to provide evidence to the agent. 6. Logging execution traces (prompts, tool calls, intermediate results) for a specialized inspector agent to categorize failures and guide harness improvements. The core lesson is that reliability often comes from the harness design around the model, especially for smaller LLMs.