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How Linnify built AI agents that simulate real work
See how we designed a multi-agent AI system for GentrAIner that prepares students for the realities of the modern workplace, not by delivering content, but by simulating the dynamics of a real job.
client
GENTRAINER
MARKET
UNITED STATES
INDUSTRY
edtech
solution
MULTI-AGENT SYSTEM
Beyond technical expertise, what truly sets Linnify apart is their partnership mindset. Linnify has shown me they are not just a software development company - they are genuine partners invested in my mission as much as I am. They celebrate wins with me, challenge ideas with creativity, and continually push boundaries to bring GentrAIner to life as a transformative product. I could not have asked for a better team to walk this journey with.
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GentrAIner is an AI-powered virtual internship platform built for the US higher education market.
Its mission is to close the career readiness gap that leaves the majority of US college students underprepared for their first job.
The platform delivers full-length, semester-aligned virtual internships where students interact with AI agents that act as real colleagues, supervisors, and evaluators.
Universities use GentrAIner to scale career readiness programs beyond the limits of traditional internship pipelines. While employers gain access to candidates with measurable, standardised competency data before they ever interview.
Linnify partnered with GentrAIner from early-stage product validation through to production deployment, contributing product strategy, full-stack engineering, experience design, and the AI architecture that powers GentrAIner's agentic simulation engine. The partnership is ongoing.
CHALLENGE
The career readiness gap is not an education problem. It is an access problem.
Only 41% of US college students have access to any internship experience before graduating. For first-generation students and those at public universities, that number drops to 27-36%. The students who do get internships earn, on average, $15,000 more in their first job than those who do not.
Aaron Meyers (GentrAIner’s founder) surveyed 177 higher education career center professionals and assessed student proficiency against NACE's eight career readiness competencies. The results were stark:
The problem is not that students lack knowledge. They lack practice. The workplace skills that matter most, professionalism, communication, judgment under pressure, are developed through experience, not coursework.
Source: Independent research survey, Aaron Meyers, 2023. Assessed against NACE career readiness competencies.
Why current alternatives are not addressing the challenge?
A traditional LMS can deliver content and track completion. It cannot simulate a supervisor who remembers what you did last week, a colleague who reacts to how you handled a conflict, or a performance review that reflects your actual behaviour across a six-week arc.
Rule-based systems and scripted chatbots collapse the moment a student does something unexpected, which is exactly what real workplaces demand.
GentrAIner needed AI that could hold context, adapt to individual students, maintain coherent personas over time, and respond naturally to the full range of human workplace behaviour. That required agents, not workflows.
Multi-agent AI system for workforce induction training
GentrAIner's simulation engine is built on four AI agents, each with a distinct role, persistent memory, and defined behavioural boundaries:
The Colleague Agents
Handles day-to-day workplace interaction: task delegation, status updates, interpersonal dynamics. They react contextually: a student who communicates well gets different responses than one who is passive or unresponsive.
The Supervisor (Boss Bot)
This is the student's primary relationship throughout the internship. It sets expectations, gives structured feedback, conducts performance reviews, and escalates based on behaviour patterns. It remembers everything and behaves accordingly.
The Technical Evaluator
Assesses domain-specific outputs: documents, decisions, presentations. It scores against professional standards calibrated by human workplace experts, not generic rubrics.
The Scenario Orchestrator
Manages the internship arc, unlocking new challenges based on performance, triggering edge cases, and ensuring the simulation evolves in response to how each student progresses.
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Building the frontend for an AI internship simulation meant designing for people, not prompts. Students had to feel like they were working with real colleagues, so every interaction with the multi-agent system needed to feel natural, from a supervisor's feedback to a teammate's quick check-in. That's what makes agentic AI for workforce training work: when the interface disappears and the experience feels like a real job.
What changed after launch?
Scale without proportional cost
GentrAIner can now run thousands of virtual internship sessions without scaling human facilitators. The bottleneck that made quality internship access scarce, human time and geographic proximity, no longer applies.
Consistent, calibrated practice for every student
Every student gets the same quality of agent interaction. Unlike real internships, where quality depends entirely on the luck of the mentor, GentrAIner guarantees calibrated, professional-standard practice regardless of background or institution.
Equity at scale
The 59% of US students without any internship access can now practice in a system that reflects diverse workplace contexts. GentrAIner's data model was built from the start to mirror US demographic diversity, not as a feature, but as a founding principle.
A platform that compounds
Because Linnify built the agentic infrastructure as a versioned, componentised aOS (agent operating system), new industry-specific internship modules can be added without rebuilding from scratch. Each new module is faster and cheaper than the last.
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Hear it directly from the people who built it
In March 2026, Linnify hosted an unofficial SXSW EDU side event at the University of Texas at Austin, using GentrAIner as a live case study in building agentic AI for real-world readiness.
The panel featured Aaron Meyers (CEO, GentrAIner), Russ Finney (Assistant Professor & Director of MIS Undergraduate Programs, McCombs School of Business, UT Austin), and Catalin Briciu (Co-Founder & Co-CEO, Linnify).
They discussed:
Why agentic AI was chosen over simpler alternatives
How agent behaviour that mirrors real colleagues was designed
The trade-off between realism, control, and predictability
What changed for students and educators after launch
Honest lessons from building this in production
This is not a polished success story. It is the kind of honest conversation product and technical leaders need before making a decision about AI.
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