How Intercom 2X'd Engineering Velocity with Claude Code | Brian Scanlan

Why It Matters

Intercom’s AI‑driven velocity boost demonstrates that generative code tools can double engineering output, signaling a new productivity frontier that other tech firms must evaluate to stay competitive.

Key Takeaways

• Intercom doubled engineering PR throughput using Claude Code AI.
• AI tools unlocked developers’ imagination, reducing coding friction dramatically.
• Organization treated as product; high‑trust culture accelerated adoption.
• Metrics show PRs per R&D head up, but CI costs rose.
• Ongoing cost‑efficiency concerns push teams toward agent‑first workflows.

Summary

Intercom’s R&D team has leveraged Anthropic’s Claude Code to dramatically accelerate its software delivery pipeline, reporting a two‑fold increase in pull‑request throughput over the past nine months. The company attributes this surge to AI‑driven code generation that removes the manual friction of typing, allowing engineers to focus on higher‑level design and innovation.

Key data points include a rise in merged PRs per R&D head, broader adoption across engineers, product managers, designers, and TPMs, and a shift in bottlenecks from CI capacity to code review. Intercom’s CTO set an explicit goal to double output, tracking metrics via telemetry in Honeycomb, Snowflake, and S3, and observed that the AI‑augmented workflow not only boosted velocity but also improved developer experience and product quality.

Notable remarks from senior principal engineer Brian Scanlan highlight cultural factors: “the physical limits of my ability to type code are unlocked by AI,” and the suggestion that granting developers PTO to experiment with AI can yield rapid skill gains. The team treats the organization itself as a product, fostering a high‑trust environment that encourages experimentation and rapid iteration.

The broader implication is that AI‑first engineering can become a competitive differentiator, but scaling such gains brings cost considerations, especially as token usage and CI expenses rise. Companies must balance the productivity upside with sustainable budgeting while moving toward an “agent‑first” development model that reimagines technical work at a higher abstraction level.

Original Description

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:

1. How Intercom doubled their merged PRs per R&D employee in just nine months using Claude Code

2. The telemetry infrastructure they built to measure AI adoption and quality across hundreds of engineers

3. Why they built a skills repository with hooks that enforce engineering standards automatically

4. How they’re preparing their product for an agent-first world with CLIs, MCPs, and ephemeral APIs

5. The permission and accountability framework that enabled rapid AI adoption

6. Why backlog zero is now achievable and what that means for engineering culture

Brought to you by:

Celigo—Intelligent automation built for AI: https://celigo.com/howIAI

Cursor—The best way to code with AI: https://www.chatprd.ai/howiai

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 [e-mail]._