The Amazon Nova AI Challenge is back for its second year. As ten selected university teams from around the world gather in Seattle this week for bootcamp (February 2-4), they're preparing to tackle a real-world challenge in software development: building AI agents that can handle complex coding tasks while maintaining security and reliability.
For the first time in an academic competition, participating teams will use Amazon Nova Forge to customize Nova models with access to tools, models and computational resources that have historically been out of reach for university research programs.
From single tasks to multi-step projects
Last year's competition focused on secure AI-assisted software development, with teams working to identify and address vulnerabilities in code-generating models. Teams published research papers on their approaches, and the results demonstrated practical methods for improving security in AI coding tools.
This year's challenge reflects how AI coding technology has progressed. "Generative AI for software development has rapidly moved from code generation to agents that plan, build, and test changes across entire codebases and user-facing applications," said Imre Kiss, Director, Amazon Nova Software Engineering Skills, "The focus of this year's Nova Challenge reflects that shift."
The 2026 challenge centers on AI agents that can work through multi-step software development tasks, including planning changes, writing code, and validating results across complex projects. Unlike generating code from a single prompt, these systems must understand context across entire codebases and make decisions that affect product quality and system security.
Teams must demonstrate progress on two measures: utility (can the agent handle increasingly complex software tasks?) and safety (does it maintain appropriate safeguards?). This dual focus addresses a practical reality: as AI agents become more capable, new security challenges emerge. Each team's approach will be different: some may focus on adding secure coding patterns to their training data, others on creating training environments that teach their agents to recognize security issues, and others on building smaller, faster models with strong agentic security reasoning.
The red teams will develop methods to test the applications built by these AI coding agents for weaknesses, attempting to identify potential vulnerabilities and exploits.
"The competition format creates an interesting dynamic," explains Rahul Gupta, Senior Applied Science Manager, Amazon Nova Responsible AI. "As red teams discover new vulnerabilities, developer teams must adapt their agents through retraining or additional safety controls. And as developer teams strengthen their systems, red teams must develop more sophisticated testing methods."
Nova Forge: Access to model customization
The significant change for this year's competition is integration of Nova Forge, Amazon's service for building customized AI models. Academic institutions have historically had limited access to the models, training data, and computational resources needed for large scale AI-research. Nova Forge changes that.
"What researchers (and entrepreneurs) have traditionally faced is a set of difficult trade-offs," explains Michael Johnston, an applied science leader at Amazon overseeing the challenge. "You could fine-tune an existing closed model, but only in limited ways. You could work with open-source models, but risk losing core capabilities. Or you could build from scratch, but only if you had very substantial funding."
Nova Forge offers another approach. The service gives teams access to Nova model checkpoints at different training stages, allowing them to add their own data throughout the training process. The result is a customized model — what Amazon calls a "Novella" — that combines Nova's capabilities with the team's specific approach to secure software development.
"This changes what's possible for academic research," says Professor Ismini Lourentzou, University of Illinois Urbana Champaign. "We're participating in the training process itself, adding our research and security methods into the model's foundation."
Nova Forge provides three capabilities that competing teams will use:
- Custom training environments: Teams can create simulated environments where models learn from scenarios that reflect real-world secure coding workflows.
- Model compression: Teams can create smaller, faster models that maintain performance at lower cost by training them on examples from larger models.
- Safety controls: Built-in tools allow teams to implement security measures and evaluate model behavior against their safety criteria.
The competing teams
Ten universities were selected to compete this year from a pool of applicants spanning five countries: the United States, Portugal, the Czech Republic, South Korea, and Taiwan. The lineup includes two returning champions and eight new teams:
Model Developer teams:
- PurpCorn, University of Illinois Urbana-Champaign - Year 1 champions
- BlueTWIZ, NOVA School of Science and Technology, Lisbon, Portugal
- AlquistCoder, Czech Technical University, Prague, Czech Republic
- BruinWeb, University of California, Los Angeles
- Slugs and Roses, University of California, Santa Cruz
Red teams:
- PurCL, Purdue University - Year 1 champions
- Jay'lBreak, Johns Hopkins University
- TeamSecLab, Ohio State University
- Pr1smCode, Carnegie Mellon University
- Lion-x0a, Penn State University
Each team receives $250,000 in sponsorship, monthly AWS credits, and the chance to compete for prizes. The winning model developer and red teams will each receive $250,000 (split among students), with second-place teams earning $100,000.
Practical research
For participating students, the challenge is an opportunity to work on problems with direct application.
"Academic research often focuses on theoretical problems," notes Xiangzhe Xu, PhD Student, Purdue University. "Here we are working with large-scale models. That changes how we approach the science."
Amazon researchers working with the teams emphasize solutions that are straightforward to implement, easy to troubleshoot, and economically viable at scale. "We want innovations that engineers can actually use," says Johnston.
The challenge also gives students experience with infrastructure not typically available in academic settings. Along with Forge, and the computational resources to train and evaluate large-scale models.
"For a university team, this level of access is significant," says Professor Xiangyu Zhang, Purdue University. "We're able to run experiments that would be difficult on our academic budget, and students are gaining experience with the same tools used by top-tier AI companies."
What's next
The first evaluation will begin after bootcamp concludes, with additional rounds scheduled through August 2026.
The finals will be in September 2026 and winners will be announced October 2026 at Amazon Nova AI Challenge Summit where teams will gather to present their research and celebrate the winning teams.
All participating teams will publish research papers on their methods and findings. These publications will contribute to the field of responsible AI development, with particular focus on secure AI systems, insights that will benefit software development and other applications where AI interacts with complex systems.
As AI systems become more capable of software development work, the research these teams are conducting becomes increasingly relevant. As AI coding systems take on more and more complex and impactful tasks, the key question is how to ensure the resulting applications are secure, reliable, and trustworthy at scale.
Stay tuned for updates on the teams' progress and coverage of theSeptember 2026 finals.
