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

Guests** Jack Taylor, Product Engineer, Gradient Labs Ibrahim Faruqi, AI Engineer, Gradient Labs In this episode The iceberg metaphor: why frontline support is only the tip of automation potential How three agent types (inbound, back office, outbound) coordinate on complex tasks like fraud disputes Natural language procedures that let subject matter experts train agents without engineering bottlenecks The "turn" architecture: state machines that orchestrate agent logic across async, multi-day conversations Skills as modular agent capabilities—and how they're scoped deterministically per turn Defining "done" for outbound agents when the customer isn't the one ending the conversation Guardrails as classification problems: balancing recall and precision for regulatory compliance Ask a Human: a tool call that brings humans into the loop for approvals or missing APIs Auto-eval pipelines that flag conversations for manual review and feed labeled datasets Links & References Gradient Labs Incident.io episode – Referenced in the conversation Chapters 00:00 Meet the Engineers: Jack and Ibrahim 00:39 The Role of Product Engineers in Tech 01:21 Introduction to Gradient Labs 02:11 The Three Pillars of Customer Support Automation 04:32 The Evolution and Growth of Gradient Labs 05:29 Building and Refining AI Agents 06:39 Outbound Agent: Addressing Customer Problems 09:12 Defining Success in Outbound Procedures 17:08 Ensuring Compliance and Guardrails 30:17 Understanding Agent Guardrails 31:54 Complexities of Natural Language Input 36:21 Skill Design and Management 39:53 Deterministic Skill Execution 41:54 Customer-Specific Guardrails 44:21 APIs and Customer Tools Integration 46:02 Ask A Human Tool 48:24 Guardrails as Classification Problems 57:12 Auto Eval System 59:12 Future of Multi-Agent Systems