Building Lorikeet: How AI Humility and a Dual-Agent Architecture Are Redefining Customer Support

May 29, 2026

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Building Lorikeet: How AI Humility and a Dual-Agent Architecture Are Redefining Customer Support

Here's the surprising part — Lorikeet’s AI support agents are designed to admit when they don’t know something, and then gracefully hand things off to humans. That’s what Teresa Torres highlights in her chat with the Lorikeet team. Instead of trying to replace human support, they built a dual-agent system: a Concierge that handles tickets end-to-end, and a Coach that continuously helps customers tune and improve the AI. Now, here’s where it gets interesting — by embedding 'AI humility' into their design, Lorikeet’s agents know when to step back, especially in complex or regulated industries. According to Torres, this approach isn’t just about smarter tech; it’s about building trust and avoiding costly mistakes. And get this — customers define their own success metrics and guardrails, making the system incredibly adaptable. So what’s next? If Lorikeet’s pattern holds, future AI support tools will be those that know their limits and work seamlessly with humans, not in spite of them.

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What does it take to build an AI customer support agent that actually knows when it can't help — and says so?

In this episode of Just Now Possible, Teresa Torres talks with Jamie Hall (Co-founder & CTO), Xharmagne Carandang (Product Engineer), and Rona Wang (Product Engineer) of Lorikeet, a startup building AI customer support concierge agents for businesses in regulated industries. Lorikeet's vision: an agent that responds like the best customer support you've ever had — one that knows you, gets things fixed, and hands off gracefully when it's out of its depth.

The team spent months exploring the wrong ideas — reflection tools, information dashboards — before a healthcare startup pulled them toward the real problem: just help us clear the inbox. Their earliest prototype was a command-line script delivering results via CSV. Today, Lorikeet runs two agents: a Concierge that handles customer tickets end-to-end, and a Coach that helps customers configure, test, and continuously improve it.

You'll hear how they built customer-configurable guardrails (and why a cannabis company's support tickets broke their first approach), designed a "resolution in the loop" pattern for human-AI collaboration, and are now flipping the configuration workflow so customers define what good looks like before they ever write a standard operating procedure.

Show Notes

Guests:

Jamie Hall, Co-founder & CTO, Lorikeet
Xharmagne Carandang, Product Engineer, Lorikeet
Rona Wang, Product Engineer, Lorikeet

In this episode:

How Lorikeet evolved from failed ops tools to a full AI customer support concierge
The dual-agent architecture: Concierge for customer tickets, Coach for configuration and ongoing improvement
Why "AI humility" — defaulting to human handoff when uncertain — is a core design principle
How Lorikeet integrates with Zendesk and Intercom instead of replacing them
The UX evolution from workflow builder to conversational interface — and why the blank chat box is still hard
"Resolution in the loop": how human agents unblock the AI without taking over a ticket
Why guardrails need to be domain-specific — the cannabis company story
How customers define their own evals and guardrails through the Coach interface
Using AI to diagnose failure modes in traces and automatically suggest fixes
Lorikeet's product engineering culture: every engineer asks weekly what they learned from a customer

Resources & Links:

Lorikeet — AI customer support concierge for enterprises in regulated industries
Gradient Labs on Just Now Possible — another AI agent team in regulated financial services
Neople on Just Now Possible — AI digital coworkers with a similar training-by-conversation approach
Incident.io on Just Now Possible — AI SRE with multi-agent hypothesis investigation

Chapters

00:00 Meet the Team
01:05 What Lorikeet Builds
02:34 Origin Story and Early Missteps
06:42 Finding the Real Support Pain
07:37 Why AI Fits Support Work
11:16 First Prototype and Early Evals
14:42 Design Partners and Selling the CLI
16:30 Product Mindset and the Real Moat
19:47 Rona Joins and Scaling Up
21:02 Milestones Voice Actions Escalation
23:48 Integrations with Zendesk Intercom
25:59 How the Agent Works Today
28:30 Coach Agent and Configuration UX
32:58 SOPs to Test Cases
34:35 Refund Flow Setup
36:12 Coach Conversational UI
38:12 Hybrid UX Guidance
40:46 Resolution in Loop
43:17 Collaboration Middle Ground
49:40 Process Maturity Limits
53:30 Confidence and Guardrails
55:59 Customer Defined Guardrails
01:01:14 Trace Diagnosis Agent
01:03:14 Product Engineers Culture
01:07:46 Closing Thoughts

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