We just published our research on what we’re calling “Machine Learning as a Tool” (MLAT) - a design pattern for integrating statistical ML models directly into LLM agent workflows as callable tools. The Problem: Traditional AI systems treat ML models as separate preprocessing steps. But what if we could make them first-class tools that LLM agents invoke contextually, just like web search or database queries? Our Solution - PitchCraft: We built this for the Google Gemini Hackathon to solve our own problem (manually writing proposals took 3+ hours). The system:
- Analyzes discovery call recordings
- Research Agent performs parallel tool calls for prospect intelligence
- Draft Agent invokes an XGBoost pricing model as a tool call
- Generates complete professional proposals via structured output parsing
- Result: 3+ hours → under 10 minutes Technical Highlights:
- XGBoost trained on just 70 examples (40 real + 30 synthetic) with R² = 0.807
- 10:1 sample-to-feature ratio under extreme data scarcity
- Group-aware cross-validation to prevent data leakage
- Sensitivity analysis showing economically meaningful feature relationships
- Two-agent workflow with structured JSON schema output Why This Matters: We think MLAT has broad applicability to any domain requiring quantitative estimation + contextual reasoning. Instead of building traditional ML pipelines, you can now embed statistical models directly into conversational workflows. Links:
- Full paper: Zenodo , ResearchGate Would love to hear thoughts on the pattern and potential applications! submitted by /u/okay_whateveer
Originally posted by u/okay_whateveer on r/ArtificialInteligence
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