Multi-Agent Architectures, Memory Management & Model Auditability (SF)
About This Course
This 5-day technical course explores the design and implementation of advanced AI/ML systems with a focus on multi-agent coordination, memory integration, and model auditability. Participants will learn how to build AI workflows where multiple models and agents interact, share memory, and operate in dynamic environments. The course covers architectural best practices, vector databases, context-aware inference, reproducibility, audit logs, and explainability. Through vendor-neutral tools and frameworks, learners will prototype modular, auditable systems capable of long-term memory use, goal-driven planning, and traceable decision-making—ready for deployment on any cloud or on-premise infrastructure.
What You'll Learn
LO1. Integrate foundational models into applications by applying prompt engineering techniques and model configurations using specialized Generative AI developer tools.
LO2. Develop low-code or no-code applications by configuring software settings and verifying system compatibility to ensure environment stability.
LO3. Apply Context Augmentation and Memory Management using Retrieval Augmented Generation within agent workflows
LO4. Troubleshoot software performance issues by applying standard diagnostic procedures and interpreting technical documentation to resolve system inefficiencies.
LO5. Analyze AI tool performance issues and error messages to differentiate between technical bugs and inherent model limitations, facilitating appropriate technical escalation.
Entry Requirements
Professional Qualification and Other Diploma
Candidates who do not meet the above requirements will be assessed on case by case basis.