How Ramp Uses Langfuse to Build AI Agents That Improve Themselves
Ramp's engineering team on tracing agents with self-hosted Langfuse, and how Reflect turns traces into merged fixes - 30% faster sessions, 15% fewer tool calls.
Highlights from the talk
Ramp is a smart financial infrastructure company serving 70,000+ businesses across cards, expense management, AP, travel, treasury, procurement, and token spend management - saving customers roughly $2B and 20 million hours. Nearly all of it now runs on agents.
In this talk, Ryan Delgado (Engineering) and David Traina (Data Platform) walk through Ramp's agent portfolio and the observability layer underneath it. Customer-facing agents include Policy Agent, which approves or denies expenses before a manager ever sees them, and Stack, which compresses month-end close from a week and a half to 1-3 days. Internally, Inspect - Ramp's in-house background coding agent - now accounts for ~70% of merged PRs at roughly 1,500 PRs per day, and Ramp Research answers data questions in Slack in 2-3 minutes instead of 3-5 hours.
"Inspect accounts for about 70% of the merged PRs within Ramp, and we're raising about 1,500 PRs a day across a few different repos.
David Traina explains why Ramp chose Langfuse: fully self-hosted on standard infra (S3, Redis, Postgres, ECS, Terraform) with ClickHouse Cloud, fully open source, OpenTelemetry-native, and API-first so agents - not just humans - can be first-class users.
He then demos Reflect, a Ramp agent that monitors other agents. Reflect scores and clusters Langfuse traces, diagnoses recurring failure patterns, and proposes fixes. Applied to 12 agents with 120+ merged suggestions, it cut Ramp Research token usage by 10-20%, tool calls by 15%, and session times by 30%.
"Reflect works by semantically scoring those traces and grouping similar traces together… it analyzes those traces in aggregate to find patterns, diagnose issues, and propose fixes for a human owner to either accept or reject. We've seen a 10-20% decrease in token usage, 15% fewer tool calls, and 30% faster end-to-end session times.
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