Beyond Vibe Coding: Leveraging Foundation Models to Infer Intent
summary
Prototype of a multi-agent ToM system uses iterative dialog, sentiment and expertise inference, and multimodal inputs to extract hidden intent and produce detailed specs; generated outputs are evaluated by LLM judges with a custom rubric and a requirement‑richness metric and scored significantly better than a single‑model baseline. The system produces significantly longer clarification dialogs (baseline skips clarification in ~60% cases), keeps high consistency with source conversation, but incurs much higher API/token cost.
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chapters (14)
- 0:00Introduction
- 3:12Vibe coding rise and limits
- 5:59Agentic systems rethink
- 8:28Intent as new source code
- 10:38Intent extraction interfaces
- 12:22Theory of mind in multi-agents
- 14:49Evaluating intent systems
- 17:20Clarifying requirements
- 18:27Measuring requirement quality
- 20:34Consistency in requirements
- 21:36Cost of multi-agent refinement
- 23:23Intent-driven challenges
- 26:55Summary and future
- 27:38Q&A
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