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

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