Built to Scale: Design Patterns for Production-Ready Agentic Systems
About this session
Generative AI has rapidly evolved from individual copilots to autonomous agents capable of planning, reasoning, collaborating, and executing complex workflows. While building a proof of concept has become easier than ever, designing production-ready agentic systems that are reliable, secure, scalable, and maintainable remains a significant engineering challenge.
This session introduces practical design patterns for building enterprise-grade agentic systems. Rather than focusing on a specific framework or vendor, we will explore reusable architectural patterns that help engineering teams solve common challenges in orchestration, tool integration, memory, human oversight, evaluation, and multi-agent collaboration.
Attendees will learn how to select the right pattern for different business problems, understand the trade-offs between latency, cost, reliability, and security, and recognize common anti-patterns that prevent AI solutions from successfully reaching production.
Through architecture diagrams, real-world engineering scenarios, and decision frameworks, participants will gain practical guidance they can apply immediately when designing or scaling agentic AI solutions.
Whether you are an AI engineer, software architect, engineering manager, or technical leader, this session provides a practical playbook for moving beyond prototypes and building production-ready agentic systems that deliver sustainable business value.
Speaker
Key takeaways
- Recognize the core design patterns used to build production-ready agentic AI systems and understand when each pattern is most effective.
- Evaluate architectural trade-offs between latency, cost, security, reliability, and human oversight when designing enterprise AI solutions.
- Apply a practical decision framework to move AI applications from prototypes to scalable, maintainable, production-ready systems.