Agentic AI: Foundations, Engineering, and Enterprise Deployment
About This Course
This course equips learners with the skills to design, build and deploy modern agentic Artificial Intelligence (AI) systems. Learners will learn core concepts of generative AI, the ReAct (reasoning and acting) paradigm and multimodal reasoning, before progressing to task planning, tool integration and retrieval-augmented generation (RAG). Through hands-on labs, learners will build single-agent and multi-agent workflows using frameworks such as LangChain, LangGraph, AutoGen, Model Context Protocol (MCP) servers and vector stores such as ChromaDB. Advanced topics include reflection loops, observability, evaluation automation, error analysis, and cost and latency optimisation. The course also covers AI safety, data governance, guardrails, human-in-the-loop design and enterprise deployment via Continuous Integration/Continuous Delivery (CI/CD) and monitoring.
What You'll Learn
• Explain the foundations of generative Artificial Intelligence (AI), multimodal reasoning and the ReAct (reasoning and acting) paradigm, and articulate how these concepts underpin agentic AI systems.
• Design, develop and evaluate single-agent and multi-agent workflows using modern agent frameworks such as LangChain, LangGraph, AutoGen and Model Context Protocol (MCP)-based tools and integrations.
• Implement task planning, tool integration and retrieval-augmented generation (RAG) to enable agents to access, ground and reason over external information sources.
• Use vector stores such as ChromaDB to build memory-augmented agents capable of context retention and information retrieval.
• Apply advanced agent design patterns, including reflection loops, observability, evaluation automation, error analysis and cost and latency optimisation.
• Implement AI safety mechanisms and data governance controls, including guardrails, safe tool access, permissioning and human-in-the-loop workflows.
• Deploy agentic AI systems in enterprise environments, incorporating Continuous Integration/Continuous Delivery (CI/CD) practices, monitoring, traceability and performance management.
• Assess the business value, risks and organisational considerations associated with adopting agentic AI solutions, enabling informed decision-making and effective implementation strategies.
Entry Requirements
- General Diploma
- Basic knowledge of Artificial Intelligence (AI) and machine learning