Automating the Full Customer Support Iceberg: How Gradient Labs Built a Multi-Agent Platform

What happens when a customer reports a stolen credit card? The frontline answer is simple—freeze it. But underneath lies a cascade of follow-ups: dispute filings, fraud investigations, merchant communications, and proactive outreach to gather more details. Most AI support tools handle only the tip of the iceberg.

In this episode, Teresa Torres talks with Jack Taylor (Product Engineer) and Ibrahim Faruqi (AI Engineer) from Gradient Labs, an AI-native startup building agents that automate the full scope of customer support in fintech. They share how they've architected a platform with three coordinating agents—inbound, back office, and outbound—all built on a shared foundation of natural language procedures, modular skills, and configurable guardrails.

You'll hear how they:

- Let non-technical subject matter experts define agent behavior through natural language procedures—no coding required - Architected a state machine orchestrator that manages turns, triggers, and skill selection across long-running conversations - Built guardrails as binary classifiers with eval pipelines, tuning for high recall on critical regulatory checks - Designed an auto-eval system that samples conversations for human review to catch edge cases and build labeled datasets

It's a detailed look at how one startup is moving beyond simple Q&A bots to agents that can actually take action, coordinate across workflows, and handle the messy reality of customer support.