This challenge assesses your ability to design and implement an AI-powered workflow assistant for PortX, a company that powers integrations between banks, credit unions, and fintech companies.
Scenario — "AI Workflow Assistant for Integration Setup":
PortX powers integrations between banks, credit unions, and fintechs. New customers often ask: "How do I connect my banking core to a fintech partner for payments or onboarding?" Right now, this setup process involves reading documentation and configuring API endpoints manually.
Your task is to design an AI Workflow Assistant that can guide users through connecting a new integration safely, interactively, and with human-level clarity.
Record a video walking us through your approach, and submit two short design documents outlining your system architecture and reliability strategy.
Design orchestration layers for LLM-powered agents
Implement safety guardrails and human-in-the-loop workflows
Build transparent, explainable AI interactions
Balance reliability with flexibility in agentic systems
Create audit trails for compliance and trust
Required Submissions:
Focus on architecture and reasoning — not full code. A hand-drawn diagram is acceptable if clear.
Strong Example: "I'd design an orchestration layer with LangChain's agent executor and custom PortX tools: ListPartnersTool for available integrations, SetupEndpointTool for authenticated API setup. Each step prompts user confirmation ('I'll create this endpoint — okay?') and logs it in Datadog. Guardrails: restrict API actions to read-only until confirmation; use retry on timeout. Success metric: 90% of users complete setup with zero support tickets."
Weak Example: "Use GPT to generate configs and push them to the API automatically."
Think about both user experience and system health.
Your 7-10 minute video must include ALL three components:
Briefly describe one AI-powered experience you've built or prototyped. What made it effective for users, and how did you validate its performance?
Which of these situations fits your natural working style better — and why?
Scenario A — "The Launch Sprint" (Execution): You're asked to ship an AI feature that summarizes customer feedback from support tickets. The models and APIs are known — your challenge is tuning prompts, handling edge cases, and delivering it into production by the end of the week. Would that kind of focused, high-velocity delivery excite you?
Scenario B — "The Discovery Loop" (Exploration): You're asked to design a new "AI Workflow Assistant" that helps financial institutions connect data pipelines to fintech partners. No one knows exactly how it should work yet — you'll explore, prototype, and define the core interaction model. Would you enjoy that kind of open-ended, product-defining work?
There's no right or wrong answer — we just want to understand how you like to work.
Walk us through how you would design and implement the AI Workflow Assistant described above:
At the end of your video, please tell us if you used AI. If you did, let us know how you used it.
On this page
