December 18, 2025

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

1 hour 1 minute

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

...more

By Teresa Torres