Program Overview
Highlights of the 2026 Winter School include an introductory seminar on contextual AI, a workshop on building agentic systems integrating human and machine intelligence, workshops exploring leveraging these systems in production, transportation, and communication, and a deep dive into the UX/UI challenges of agentic systems.
Course Modules
Context is all you Need
The opening seminar explores the critical importance of adapting generative AI models to specific local contexts across enterprise environments. Participants will gain strategic insights into the foundational Module Context Protocol that enables effective communication between human and machine agents within organizational structures.
Key Learning Outcomes:
- Understanding the Module Context Protocol for human-machine communication
- Codex adaptation frameworks for industry-specific needs
- Localizing AI models to regulatory environments and operational workflows
- How OpenAI agents with contextual awareness transform business operations
From Case Studies to Use Cases
This hands-on workshop will cover the necessary topics to build business-ready applications of contextual AI in the fields of communication, transportation and production. Participants will review the case studies developed in the BAI white paper, model an agentic system applying in-context learning and positional encoding techniques to address specific challenges, and document realistic use cases moving forward.
Key Learning Outcomes:
- Building business-ready AI applications across multiple industries
- Applying in-context learning techniques to real challenges
- Using positional encoding for improved model performance
- Documenting and planning realistic AI use cases
Building Agentic Systems
This workshop is designed to help participants work with large language models and application frameworks in developing AI agents that can sense, reason, communicate and act. Under the supervision of a Corporate Project Manager, participants will model realistic scenarios for executives and learn strategies for balancing standardized AI capabilities with localized adaptation requirements.
Key Learning Outcomes:
- Working with large language models and application frameworks
- Developing AI agents with sensing, reasoning, and communication capabilities
- Creating governance structures for continuous contextual learning
- Developing metrics to evaluate contextual performance in production
Enhancing the Explainability of AI Models
This workshop addresses the critical importance of explainability, transparency and trust in AI, exploring how responsible AI practices can enhance the performance of agentic systems while aligning with the requirements of current regulatory frameworks. Participants will gain hands-on experience applying best practices to AI use cases.
Key Learning Outcomes:
- Understanding AI transparency and explainability requirements
- Applying responsible AI practices to agentic systems
- Aligning AI development with regulatory frameworks
- Leveraging human intelligence through explainable AI
Designing the UX/UI of Collaborative Intelligence
Industry experts will address specific UX/UI challenges of agentic systems. Designing systemic features that enhance trust, explainability, and transparency are not just nice-to-have features—they are critical success factors in building contextual AI. Using examples from their current assignments, the facilitators will discuss the challenges of modeling agentic systems to capture, amplify, and communicate the context cues critical to improving personal and organizational decision-making.
Key Learning Outcomes:
- Understanding UX/UI challenges specific to agentic systems
- Designing features that enhance trust and transparency
- Capturing and communicating context cues effectively
- Improving decision-making through intelligent interface design
Hands-On Learning with Industry Experts
All modules combine theoretical frameworks with practical, hands-on workshops led by industry practitioners and academic experts. You'll work with real-world case studies, develop actual AI implementations, and learn from professionals actively deploying agentic systems in enterprise environments.
Each workshop builds upon the previous modules, creating a comprehensive learning journey from understanding contextual AI fundamentals to designing and implementing production-ready agentic systems with proper governance, explainability, and user experience considerations.
Ready to Master Agentic Systems?
Join the Winter School 2026 cohort and gain hands-on experience with the frameworks, tools, and methodologies shaping the future of AI in the workplace.