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Brian Scanlan is a Senior Principal Systems Engineer at Intercom, where he works on platform engineering, developer productivity, and AI adoption across the company.
In this session from DX Annual, Brian shares how Intercom set out to double engineering throughput and ultimately achieved that goal in nine months. Rather than treating AI as an optional productivity tool, the company standardized on Claude Code, updated performance expectations, invested heavily in enablement, and adopted an agent-first approach to technical work.
Brian explains why Intercom views Claude Code as a platform rather than a tool, how the company is building domain-specific skills and workflows for agents, and why it believes agents should eventually be able to perform any technical task a senior engineer can complete on a laptop.
He also shares the data behind Intercom's AI adoption efforts, including gains in throughput, reductions in defect backlogs, improvements in code quality, and the growing use of automated pull request approvals. Throughout the talk, Brian offers a practical look at what it takes to scale AI adoption across a large engineering organization and the lessons Intercom has learned along the way.
Where to find Brian Scanlan:
• LinkedIn: https://www.linkedin.com/in/scanlanb
• X: https://x.com/brian_scanlan
• Website: https://brian.scanlan.ie
In this episode, we cover:
(00:00) Intro
(02:54) Intercom’s goal of doubling throughput
(07:30) The platform strategy
(09:30) Their agent-first strategy
(10:58) Evergreen capabilities vs custom tooling
(12:28) How Intercom works with agents
(16:43) What the data reveals about AI adoption and impact
(19:20) Using session data to improve AI workflows
(20:20) Cutting the defect backlog in half
(22:44) Inside Intercom’s Claude Code setup
(28:09) Claude Code beyond engineering
(30:49) Q&A #1: Token cost
(32:52) Q&A #2: Preparing for AI pricing changes
(34:14) Q&A #3: Stress testing and auditing skills
(36:31) Q&A #4: Criteria for agents approving PRs
Referenced:
• Intercom
• Software? No Way. We’re an A.I. Company Now! - The New York Times
• Anthropic
• Snowflake
• Linear
• LaunchDarkly
• Fin AI
• Microsoft Copilot
• Cursor
• Claude Code | Anthropic's agentic coding system
• Steve Yegge (@Steve_Yegge) / Posts / X
• Honeycomb
• Fin Ideas
• Fin CLI | AI Agent Command Line Interface
By DX5
3838 ratings
Brian Scanlan is a Senior Principal Systems Engineer at Intercom, where he works on platform engineering, developer productivity, and AI adoption across the company.
In this session from DX Annual, Brian shares how Intercom set out to double engineering throughput and ultimately achieved that goal in nine months. Rather than treating AI as an optional productivity tool, the company standardized on Claude Code, updated performance expectations, invested heavily in enablement, and adopted an agent-first approach to technical work.
Brian explains why Intercom views Claude Code as a platform rather than a tool, how the company is building domain-specific skills and workflows for agents, and why it believes agents should eventually be able to perform any technical task a senior engineer can complete on a laptop.
He also shares the data behind Intercom's AI adoption efforts, including gains in throughput, reductions in defect backlogs, improvements in code quality, and the growing use of automated pull request approvals. Throughout the talk, Brian offers a practical look at what it takes to scale AI adoption across a large engineering organization and the lessons Intercom has learned along the way.
Where to find Brian Scanlan:
• LinkedIn: https://www.linkedin.com/in/scanlanb
• X: https://x.com/brian_scanlan
• Website: https://brian.scanlan.ie
In this episode, we cover:
(00:00) Intro
(02:54) Intercom’s goal of doubling throughput
(07:30) The platform strategy
(09:30) Their agent-first strategy
(10:58) Evergreen capabilities vs custom tooling
(12:28) How Intercom works with agents
(16:43) What the data reveals about AI adoption and impact
(19:20) Using session data to improve AI workflows
(20:20) Cutting the defect backlog in half
(22:44) Inside Intercom’s Claude Code setup
(28:09) Claude Code beyond engineering
(30:49) Q&A #1: Token cost
(32:52) Q&A #2: Preparing for AI pricing changes
(34:14) Q&A #3: Stress testing and auditing skills
(36:31) Q&A #4: Criteria for agents approving PRs
Referenced:
• Intercom
• Software? No Way. We’re an A.I. Company Now! - The New York Times
• Anthropic
• Snowflake
• Linear
• LaunchDarkly
• Fin AI
• Microsoft Copilot
• Cursor
• Claude Code | Anthropic's agentic coding system
• Steve Yegge (@Steve_Yegge) / Posts / X
• Honeycomb
• Fin Ideas
• Fin CLI | AI Agent Command Line Interface

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