Brex’s AI Hail Mary — With CTO James Reggio

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Jan 17, 2026

James Reggio, CTO of Brex and leader of their AI transformation, shares his journey from mobile engineer to fintech innovator. He discusses Brex's unique three-pillar AI strategy aimed at enhancing corporate workflows, operational compliance, and customer-oriented product features. Reggio reveals how SOP-driven agents outperform traditional reinforcement learning in automating processes like KYC and underwriting. He emphasizes empowering employees to create their own AI tools and the advantages of a multi-agent network architecture in financial operations.

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INSIGHT

Three-Pillar AI Strategy

Brex splits AI into three linked pillars: corporate, operational, and product AI to align investments and impact.
This framework creates a feedback loop where product features support customers' corporate AI strategies.

ANECDOTE

Quitters Welcome Hiring Story

James considered leaving to start another company but accepted the CTO role and launched a "Quitters Welcome" EVP celebrating employees who later found startups.
He sees hiring ex-founders as valuable because Brex gives them immediate scale and real customers to build on.

ADVICE

Run A Startup-Style AI Squad

Create a small, focused AI team off to the side to act like a startup within the company and accelerate LLM product work.
Pair AI-native juniors with staff engineers who know the product to balance experimentation and product knowledge.

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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

—

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