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
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
