Agentic AI Hiring Assistant
Research and design for an agentic assistant that qualifies candidates, books interviews, and surfaces top matches so employers can make faster, more confident hires.
Company
Jobcase
Role
Senior Product Designer
Timeline
2025
Type
Research & Design Exploration
Background
Jobcase is a platform dedicated to empowering workers, with over 100 million members connecting to find jobs, share advice, and navigate work. It operates as a two-sided marketplace. Members manage job searches and access community support, while employers post roles, engage candidates, and manage hiring.
This case study focuses on improving the employer experience, where I designed an AI assistant to reduce hiring friction for small business owners and resource-limited teams.
Problem
Employers were stuck doing repetitive, manual hiring tasks with no smart tools to help.
Small business employers using Jobcase struggled with repetitive tasks like screening, scheduling, and follow-up. They lacked tools that offered intelligent automation while maintaining control and transparency over the process.
Hypothesis
"If I design an AI assistant that can independently screen candidates, coordinate interviews, and communicate through natural, accessible interfaces, while giving employers visibility and control, I can reduce time-to-hire, increase engagement, and improve hiring outcomes for resource-limited teams."
Research
Employers wanted automation they could trust, control, and understand.
I ran research sessions with 9 employers through UserTesting.com to validate value propositions, scheduling flows, and comfort with AI autonomy. Key findings:
- Employers wanted visibility and optional override, not full automation
- Email and web were preferred channels for professionalism and clarity
- Scheduling needed to be fast and low effort
- The assistant's tone needed to feel human, not robotic
The clearest signal was about autonomy. Employers didn’t want the AI to replace their judgment, they wanted it to do the repetitive work while staying inspectable. That finding drove every major design decision: rejection reasons exposed in the dashboard so screening decisions could be reviewed, fallback paths so an employer could step in and override at any point, an assistant voice written to sound human over email and web, and self-scheduling defaults that removed coordination without removing control.
Solution
I designed an assistant that could draft, screen, schedule, score, and communicate across modalities.
01
Candidate Scoring Dashboard
A visual system for the AI to tag and score applicants based on role fit, giving employers an instant read on candidate quality without manual screening.
02
Dashboard Breakdown
- 1
KPI summary strip
Layered detail lets managers start with summary rows, drill into charts, and open full breakdowns only when they need them.
- 2
Bar chart
Proportional bars show scannable candidate drop-off stage by stage.
- 3
AI-surfaced insight
AI-generated insights surface the biggest drop-off as a system recommendation, no manual analysis required.
- 4
Stage breakdown
Rejection reasons like resume mismatch or screener filter make AI screening decisions easy to review.
03
AI Candidate Screening Chat
A conversational interface designed for the AI to qualify applicants through chat before presenting interview options, filtering candidates before any employer time was spent.
04
Interview Scheduling
After passing AI screening, candidates would be prompted to self-schedule an interview with the hiring manager, removing back-and-forth entirely.
What Happened To The Work
The screening logic shipped. The interface layer didn’t.
Engineering shipped the screening logic as a background system, and the company chose to keep it background-only to prove the automation bet on a smaller surface. The scoring dashboard, the screening chat, and the self-scheduling flow stayed in design.
That outcome sharpened the problem I actually want to work on. Chat is easy mode for AI trust: the system can explain itself conversationally, one answer at a time. A background system can’t. When the user never talks to the agent, legibility has to be designed into surfaces the agent doesn’t own, the dashboards, notifications, and audit trails around it. Making an agent legible when nobody is in the conversation is the harder version of everything this project was about, and it’s the problem I’m most interested in solving next.
Results
What the research validated
9 / 9
Employers validated the prototype in research, every participant confirming it would significantly improve their hiring outcomes.
What shipped
0K+
Candidates screened before reaching hiring managers by the background screening system engineering built. The interface layer stayed in design.
Business Impact
De-risking agentic AI for the revenue side of the marketplace.
Employers are Jobcase’s revenue side, and agentic automation there is a high-stakes bet: too little autonomy and nothing gets faster, too much and employers stop trusting their own pipeline. The nine-employer study gave leadership evidence about exactly where that line sits. Employers wanted the repetitive work automated but the judgment inspectable, with visibility and optional override rather than full autonomy. That finding de-risked a strategic bet on agentic automation for the revenue-generating half of the marketplace and shaped how much autonomy Jobcase was willing to give AI in employer-facing workflows, including the decision to ship screening as a background system first. The research holds regardless of which surfaces got built: it defined the trust boundary any employer-facing agent at Jobcase has to operate within.