Rethinking how we Scaffold AI Agents - Rahul Sengottuvelu, Ramp
Mar 19, 2025 · 16:32
Rahul Sengottuvelu, Head of Applied AI at Ramp and co-founder of Cohere.io, argues that building AI agents should follow the 'bitter lesson' from AI research: systems that scale with compute outperform handcrafted, deterministic code. He illustrates this with Ramp's switching report agent, which ingests arbitrary CSV files from third-party card providers. Three approaches are compared: manually coding parsers for the 50 most common vendors, using LLMs only for column classification, and a fully LLM-driven method where the model writes and runs pandas code via a code interpreter, repeated 50 times in parallel. The last approach, though using 10,000x more compute, costs under a dollar and generalizes better, saving Ramp far more in failed transactions. Sengottuvelu also demonstrates a prototype email client where the backend is an LLM with access to a code interpreter and the user's Gmail token; the LLM renders the UI as Markdown and handles clicks by re-prompting itself, simulating a full web app without traditional backend code. He contends that as models exponentially improve, shifting more execution into 'fuzzy' LLM compute—rather than rigid code—lets builders ride the trend for…
