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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
Full Transcript
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