How Intercom 2x’d their engineering velocity in 9 months with Claude Code | Brian Scanlan

Brian Scanlan is a senior principal engineer at Intercom, where he’s led the company’s transformation to AI-first engineering. In just nine months, Intercom doubled their R&D throughput while maintaining code quality, with 100% of engineers—plus designers, PMs, and TPMs—now shipping code via Claude Code. What you’ll learn: - How Intercom doubled their merged PRs per R&D employee in just nine months using Claude Code - The telemetry infrastructure they built to measure AI adoption and quality across hundreds of engineers - Why they built a skills repository with hooks that enforce engineering standards automatically - How they’re preparing their product for an agent-first world with CLIs, MCPs, and ephemeral APIs - The permission and accountability framework that enabled rapid AI adoption - Why backlog zero is now achievable and what that means for engineering culture — Brought to you by: Celigo—Intelligent automation built for AI Cursor—The best way to code with AI — In this episode, we cover: (00:00) Introduction to Brian Scanlan (02:40) Why Intercom went all-in on AI for both product and engineering (05:01) The breakthrough moment with Opus 4.6 and Christmas break 2025 (07:02) Demo: Intercom’s merged PRs per R&D head (12:50) Agent-first work as a fundamental reimagining of technical workflows (14:27) The cost tradeoff: treating AI spend as an investment (16:47) Measuring quality (21:22) Demo: Shipping a redirect in the Rails monolith with Claude Code (24:03) Creating a custom PR skill (26:33) Building a software factory with predictable quality standards (30:15) Telemetry infrastructure: Honeycomb for skill usage tracking (32:10) Session data collection and personalized usage insights (36:08) Quick overview (39:20) Walking through Intercom’s skills repository (42:16) Deep dive: The flaky spec skill and how it reached 100x capability (46:44) The “and then” workflow for building comprehensive skills (52:31) The live website and overview of workflows (53:32) How internal AI experience informs customer product decisions (56:18) Making SaaS products agent-friendly with CLIs and helpful hints (01:03:49) Why conversion drop-off is invisible in agent-driven workflows (01:05:28) Lightning round and final thoughts — Detailed workflow walkthroughs from this episode: • How Intercom Doubled Engineering Output: Brian Scanlan's 4 AI Workflows for Claude Code: https://www.chatprd.ai/how-i-ai/how-intercom-doubled-engineering-output-brian-scanlan-ai-workflows-for-claude-code • Design an Agent-Friendly CLI to Automate SaaS Product Onboarding: https://www.chatprd.ai/how-i-ai/workflows/design-an-agent-friendly-cli-to-automate-saas-product-onboarding • Build a Self-Improving AI Agent to Automatically Fix Flaky Tests: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-agent-to-automatically-fix-flaky-tests • Automate High-Quality Pull Request Descriptions with a Custom AI Skill: https://www.chatprd.ai/how-i-ai/workflows/automate-high-quality-pull-request-descriptions-with-a-custom-ai-skill — Tools referenced: • Claude Code: https://claude.ai/code • Cursor: https://cursor.com/ • Honeycomb: https://www.honeycomb.io/ • Snowflake: https://www.snowflake.com/ • Fin AI: https://www.intercom.com/fin • Vercel: https://vercel.com/ — Other references: • Intercom GitHub Repo: https://github.com/intercom • Google API Go Client Repo: https://github.com/googleapis/google-api-go-client — Where to find Brian Scanlan: X: https://x.com/brian_scanlan LinkedIn: https://www.linkedin.com/in/scanlanb/ Company: https://www.intercom.com — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [redacted email].