AgentVersa AI Research Internship

Why this internship exists

An AI response can sound convincing while overlooking evidence, exceeding its authority, or failing to acknowledge uncertainty. AgentVersa gives students a structured way to investigate those behaviors and ask where human judgment is needed.

The purpose is to develop practical skills in agent design, critical evaluation, and responsible AI research. Students learn to support their conclusions with observed outputs, consider alternative explanations, and explain the limits of what they have found.

How the learning happens

1. Design an agent. Define its role, objectives, responsibilities, constraints, and when it should seek human review. Extensive coding experience is not required.
2. Record expectations. Preserve the original design and, where practical, predict behavior before seeing the results.
3. Study connected scenarios. Examine how participating agents respond to shared case materials and to one another, including cooperation and disagreement.
4. Analyze the evidence. Compare expectations with actual outputs. Separate scenario facts, agent claims, and the student’s own interpretation.
5. Propose improvements. Use observations to develop a Version 2 design proposal and explain what further testing it would need.

Two ways students participate

Simulation Fellows: The current program design provides ten places for students whose agents participate directly in the official simulation, normally covering one agent per role. Fellows study their agents’ behavior alongside the wider interaction.

Open Research Participants: Applicants whose agents are not selected can continue the research and portfolio work using shared scenario and episode evidence. They distinguish the participating agents’ observed behavior from predictions about their own designs.

Both tracks follow the research rubric and develop a final portfolio. Selection determines direct simulation participation; it does not determine who can contribute thoughtful analysis.

What students produce

• A documented original agent design and a proposed revised design.
• A GitHub research journal with predictions, scenario observations, and evidence-based analysis.
• A final portfolio, research report, and presentation explaining patterns, limitations, and questions for further study.

The portfolio gives students concrete work to discuss with educators and employers: how they framed a problem, evaluated AI behavior, and justified their conclusions.

For industry, educators, and sponsors

This program creates opportunities to connect AI education with the practical challenge of evaluating agent behavior. We welcome conversations about:

• Industry mentorship: Help students ask useful evaluation questions and communicate findings clearly.
• Academic collaboration: Contribute research guidance, portfolio feedback, and interdisciplinary perspectives.
• Sponsorship: Discuss support for student participation, simulation resources, mentorship, and research showcases.

Specific partnership and sponsorship arrangements can be discussed with Fifty Is Nifty.

Research with clear limits

This is an educational, exploratory simulation. Its outputs do not establish that an agent is safe for real-world deployment or competent to perform legal work. Proposed redesigns require further testing before improvement can be claimed.

Student work may help inform future research, subject to appropriate permissions, contribution recognition, and review of the evidence. Publication is a possibility, not a promised outcome.