How Ramp built an AI agent that can think outside of tokens | Alex Shevchenko

Alexander Shevchenko is the head of applied research at Ramp, where he leads Ramp Labs – the team behind Ramp Sheets and a steady stream of public AI engineering experiments. Ramp Sheets started as an internal process mining tool that turned Loom videos of accountants into Markov diagrams, before evolving into the agentic spreadsheet editor that shipped in November. In this conversation, Alex walks through the architecture under the hood, why Ramp biases the agent toward Excel formulas over Python code gen, and two recent Labs experiments: Latent Briefing and a user-steerable revival of Golden Gate Claude.

We also discuss:

• Under the hood of Ramp Sheets
• Inspect, Ramp's internal coding agent, and the self-improving monitor loop it powers
• Why finance professionals rejected code gen as too "black box"
• Why Anthropic models tend to excel at agentic spreadsheet manipulation
• The case for putting the agent outside the sandbox, not inside it
• The Loom-to-Markov-diagram process mining pipeline
• RLMs and how subagents can share memory in latent space
• Latent Briefing and KV-cache communication between subagents
• Reviving Golden Gate Claude with steering vectors on Gemma

Referenced:

• Alex Levinson
• Anthropic
• Ben Geist
• Claude
• Efficient Memory Sharing for Multi-Agent Systems via KV Cache Compaction (Ben Geist)
• Gemma
• Golden Gate Claude
• Graphviz
• Inspect
• Latent Briefing
• Loom
• Modal
• OpenAI
• Opus
• Qwen
• Ramp
• Ramp Labs
• Ramp Sheets
• Recursive Language Models (Alex Zhang)
• Retool
• Self-maintaining Ramp Sheets
• Steer AI

Where to find Alex:

• LinkedIn
• Twitter/X
• Website

Where to find Harrison:

• LinkedIn
• Twitter/X

Where to find LangChain:

• Website
• Docs

Send feedback or questions to [email protected]

Timestamps:

00:00 Introduction

01:13 The origin of Ramp Sheets

02:27 The Loom-to-Markov-diagram process mining pipeline

04:28 Why code gen approaches felt too "black box" to finance

06:13 Meeting finance where they already are: inside the spreadsheet

09:08 How far process mining got them

10:31 Text descriptions and Graphviz DAGs as output

12:41 Under the hood of Ramp Sheets

14:52 Why the agent uses Python only as an escape hatch

15:47 Why Anthropic models excel at agentic spreadsheet manipulation

17:12 Frankensteining the OpenAI Agents SDK

17:43 The Ramp Sheets UX and fast vs. expert mode

19:58 Agent in a sandbox vs. agent with a sandbox

21:55 Vibe evals with expert humans

23:40 Inspect, the internal coding agent

24:13 The self-monitoring loop and auto-PRs

28:01 Other wacky experiments on Sheets

28:43 Memory experiments that didn't pan out

31:16 Latent Briefing and KV-cache subagent communication

35:13 Reviving Golden Gate Claude

37:47 Contrastive pairs and steering vectors

39:47 Picking the right layers in Gemma

41:37 What Ramp Labs looks for when hiring