Architecting for AI Success

This webinar explores cutting-edge strategies for architecting AI systems that thrive in dynamic environments, emphasizing adaptability, discoverability, and resilience through distributed systems principles. Devlin Liles will demonstrate how to: **Design for continuous evolution** - Implement _modular architectures_ with feedback loops that enable \adaptation to shifting requirements and operational conditions. - Apply _hybrid agent architectures_ combining reactive decision-making for immediate responses and deliberative planning for long-term optimization - Leverage _AI-powered monitoring agents_ to detect architecture drift, validate changes against standards, and map dependency impacts pre-deployment **Enhance discoverability and adoption** - Integrate AI capabilities through _context-aware interfaces_ and natural language interactions that surface features when relevant - Balance automation with _explainable AI patterns_ that make system capabilities and limitations transparent to stakeholders     **Build distributed resilience** - Translate distributed systems strategies like _redundant microservices_ and _failure domain isolation_ to AI component design - Implement _automated recovery cascades_ where AI agents reroute workflows, rebalance loads, and trigger backups during disruptions - Adopt _plasticity principles_ enabling systems to not just recover from failures but improve functionality through adaptation in the design of agents