Programme Content
This programme blends cutting-edge theory, AI-native implementation practices, and real-world enterprise applications to equip participants with the skills to design, orchestrate, and deploy intelligent AI systems and agents.
Introduction to Generative and Agentic AI
Intuitive understanding of Generative AI, Agentic AI, and AI-native systems Relationship between traditional AI, Generative AI, autonomous agents, and human-AI operating models Converting business challenges into scalable Generative and Agentic AI solutions Frameworks for enterprise problem-solving using Generative and Agentic AI Industry use cases: media, marketing, finance, healthcare, operations, and enterprise automation Business transformation using AI agents, orchestration frameworks, and autonomous workflows
Generative AI Fundamentals
Applications: multimodal generation, personalization, simulation, and AI-assisted productivity Techniques: Prompt Engineering, Context Engineering, Retrieval-Augmented Generation (RAG), and instruction fine-tuning Hands-on with Claude Code, LangChain, and AI-native development workflows Understanding local models, private AI deployment, and enterprise-ready AI architectures
Agentic AI Fundamentals
Autonomous reasoning and decision-making systems Planning agents, deep agents, multi-agent systems (MAS), and adaptive workflows Protocol ecosystems: MCP, A2A, tool orchestration, and agent communication frameworks Building AI agents, SQL agents, and understanding modern agent architectures Applications: supply chain optimization, enterprise operations, resource planning, and intelligent customer engagement
Model Development and Deployment
Data preparation and context pipelines for Generative and Agentic AI systems Feature extraction, engineering, evaluation, and optimization strategies AI harness engineering, testing, observability, and scalable deployment practices Integrating AI into enterprise workflows using APIs, tools, orchestration layers, and agents Deploying cloud, hybrid, and local AI systems for enterprise applications
AI Governance, Risk, Compliance, and Responsible AI
Data privacy, security, governance, and quality considerations for enterprise AI AI GRC frameworks, regulatory compliance, and responsible AI deployment practices Ethical AI considerations: mitigating bias, reducing hallucinations, ensuring transparency, and human oversight Anthropic Constitutional AI and Mythos-inspired approaches to aligned AI systems Sustainable AI practices: efficient model usage, local AI architectures, and environmental impact reduction
Building an AI-Driven Organization
Setting up teams for Generative AI, Agentic AI, and AI-native transformation initiatives Defining roles and responsibilities for AI engineering, governance, and business teams Organizational models: centralized, federated, and AI-native operating structures Upskilling strategies: balancing fresh talent, internal capability building, and human-AI collaboration Designing human-AI operating models for enterprise-scale adoption and transformation
