From building internal AI labs to becoming CTO of Brex, James Reggio has helped lead one of the most disciplined AI transformations inside a real financial institution where compliance, auditability, and customer trust actually matter.

We sat down with Reggio to unpack Brex’s three-pillar AI strategy (corporate, operational, and product AI) [https://www.brex.com/journal/brex-ai-native-operations], how SOP-driven agents beat overengineered RL in ops, why Brex lets employees “build their own AI stack” instead of picking winners [https://www.conductorone.com/customers/brex/], and how a small, founder-heavy AI team is shipping production agents to 40,000+ companies. Reggio also goes deep on Brex’s multi-agent “network” architecture, evals for multi-turn systems, agentic coding’s second-order effects on codebase understanding, and why the future of finance software looks less like dashboards and more like executive assistants coordinating specialist agents behind the scenes.

We discuss:

Brex’s three-pillar AI strategy: corporate AI for 10x employee workflows, operational AI for cost and compliance leverage, and product AI that lets customers justify Brex as part of their AI strategy to the board
Why SOP-driven agents beat overengineered RL in finance ops, and how breaking work into auditable, repeatable steps unlocked faster automation in KYC, underwriting, fraud, and disputes
Building an internal AI platform early: LLM gateways, prompt/version management, evals, cost observability, and why platform work quietly became the force multiplier behind everything else
Multi-agent “networks” vs single-agent tools: why Brex’s EA-style assistant coordinates specialist agents (policy, travel, reimbursements) through multi-turn conversations instead of one-shot tool calls
The audit agent pattern: separating detection, judgment, and follow-up into different agents to reduce false negatives without overwhelming finance teams
Centralized AI teams without resentment: how Brex avoided “AI envy” by tying work to business impact and letting anyone transfer in if they cared deeply enough
Letting employees build their own AI stack: ChatGPT vs Claude vs Gemini, Cursor vs Windsurf, and why Brex refuses to pick winners in fast-moving tool races
Measuring adoption without vanity metrics: why “% of code written by AI” is the wrong KPI and what second-order effects (slop, drift, code ownership) actually matter
Evals in the real world: regression tests from ops QA, LLM-as-judge for multi-turn agents, and why integration-style evals break faster than you expect
Teaching AI fluency at scale: the user → advocate → builder → native framework, ops-led training, spot bonuses, and avoiding fear-based adoption
Re-interviewing the entire engineering org: using agentic coding interviews internally to force hands-on skill upgrades without formal performance scoring
Headcount in the age of agents: why Brex grew the business without growing engineering, and why AI amplifies bad architecture as fast as good decisions
The future of finance software: why dashboards fade, assistants take over, and agent-to-agent collaboration becomes the real UI

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James Reggio

X: https://x.com/jamesreggio
LinkedIn: https://www.linkedin.com/in/jamesreggio/

Where to find Latent Space

X: https://x.com/latentspacepod
Substack: https://www.latent.space/

Chapters

00:00:00 Introduction
00:01:24 From Mobile Engineer to CTO: The Founder's Path
00:03:00 Quitters Welcome: Building a Founder-Friendly Culture
00:05:13 The AI Team Structure: 10-Person Startup Within Brex
00:11:55 Building the Brex Agent Platform: Multi-Agent Networks
00:13:45 Tech Stack Decisions: TypeScript, Mastra, and MCP
00:24:32 Operational AI: Automating Underwriting, KYC, and Fraud
00:16:40 The Brex Assistant: Executive Assistant for Every Employee
00:40:26 Evaluation Strategy: From Simple SOPs to Multi-Turn Evals
00:37:11 Agentic Coding Adoption: Cursor, Windsurf, and the Engineering Interview
00:58:51 AI Fluency Levels: From User to Native
01:09:14 The Audit Agent Network: Finance Team Agents in Action
01:03:33 The Future of Engineering Headcount and AI Leverage

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