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Move beyond AI experimentation and learn how modern teams are building intelligent applications that operate at production scale.
Join MongoDB and LangChain during Chicago Tech Week for an exclusive, hands-on experience designed for developers, architects, and engineering leaders. Through technical workshops, live demonstrations, and practical implementation guidance, you'll explore the architectures, tools, and patterns powering today's most impactful AI applications.
Whether you're building AI agents, retrieval systems, or intelligent customer experiences, you'll leave with new skills, proven implementation strategies, and a clear path from prototype to production.
Spots are limited, reserve your spots today!
Please note: AI Builder Day is intentionally designed for enterprise developers, architects, and technical decision-makers working on real-world AI initiatives. To maintain a highly interactive experience and maximize peer learning opportunities, registrations may be reviewed and prioritized based on fit with the event audience.
Hands-on Workshop: Building AI Agents with MongoDB and LangGraph
Agents aren't a new paradigm in AI anymore; they're the new baseline. In this workshop, you will learn the core concepts of AI agents, such as reasoning, tools, and memory. You will also learn about different agent architectures, and finally, you will build a technical documentation agent that uses MongoDB as the agent's memory and knowledge store, Claude Sonnet as the agent’s “brain,” and LangGraph to orchestrate the end-to-end agentic workflow.
Skill badge: MongoDB AI Agents with LangChain
Hands-on Workshop: Designing Memory Systems for AI Agents
AI agents need memory to maintain context across sessions, learn from experience, and handle long-running tasks. The challenge? Deciding what to remember, where to store it, and how to retrieve it when it matters. In this workshop, you'll learn a practical framework for architecting memory systems that actually work in production.
We'll cover:
- Types of memory in agentic systems
- Storage patterns: Where to persist memories and how to structure them for retrieval
- Retrieval strategies: Combining vector search with metadata, recency, and other signals
- Memory lifecycle: When to create, update, or prune memories to keep your system performant
You'll apply this framework by building memory into an AI agent and seeing how different design choices impact behavior.
Skill badge: Memory for AI Applications
AI Architecture in Action: From Concept to Production with Pete Johnson
Moving an AI app from proof of concept to production requires more than just selecting a model. In this session, we'll examine three real-world AI patterns, focusing on decisions around retrieval, memory, agent orchestration, and scalability. Each case highlights business challenges, implementation strategies, and trade-offs in building production AI. You'll gain practical frameworks, proven patterns, and insights into operationalizing AI.
What you'll learn:
- Common AI application patterns being deployed today
- Architectural considerations for moving beyond the prototype phase
- Key tradeoffs teams face when designing production AI systems
- Practical approaches to retrieval, memory, and agent workflows
- How to evaluate your own AI initiatives and prepare for production
Format:
Three real-world AI use cases, each explored through the lens of business objectives, architecture decisions, and lessons learned from the journey to production.
AI Builder Day equips developers and architects with practical experience building production-ready AI applications using MongoDB and LangChain. Attendees can earn official MongoDB AI Skills Badges through Credly, gain hands-on expertise in modern AI architectures, and return with actionable insights that can be applied immediately to current initiatives.
By attending, you'll return to your organization with practical knowledge, recognized credentials, and actionable next steps to help move AI initiatives from proof of concept to production.
Pete Johnson is Field CTO, AI at MongoDB, where he helps enterprises design and operationalize AI-driven architectures spanning large language models, vector search, and emerging agentic patterns. A 30-plus-year industry veteran, Pete has held senior leadership roles including HP.com Chief Architect at Hewlett-Packard, Principal Architect at Cisco, and AI Field CTO at CDW. He is a sought-after speaker known for translating complex AI and data platform decisions into clear business trade-offs for executive and technical audiences. His sessions frequently focus on modernization from RDBMS to NoSQL, embedding strategy for vector search, and the current state of AI agents in production.
