Generative AI Agents Design & Implementation Training Course

Course Overview

Learning Objectives

• By the end of the course, participants will be able to:
• Understand the architecture and operating principles of Generative AI Agents.
• Identify suitable business use cases for Generative AI Agents.
• Design agent workflows based on business objectives and operational requirements.
• Apply effective prompt and instruction design techniques for AI agents.
• Integrate large language models with external tools, APIs, and enterprise systems.
• Implement memory, context management, and knowledge retrieval mechanisms.
• Apply retrieval-augmented generation to improve agent accuracy and relevance.
• Design single-agent and multi-agent solutions for complex workflows.
• Implement human oversight, approval, and escalation mechanisms.
• Evaluate agent accuracy, reliability, safety, and task-completion performance.
• Establish security, governance, monitoring, and responsible AI controls.
• Develop practical strategies for deploying and scaling Generative AI Agent solutions.

Who Should Attend

Learning Outcomes

• Upon completion of the course, participants will be able to:
• Assess Generative AI Agent architectures for different business requirements.
• Identify and prioritize practical agent use cases.
• Design agent workflows and task-execution logic.
• Develop effective prompts, system instructions, and agent behaviors.
• Integrate AI agents with APIs, databases, enterprise applications, and external tools.
• Implement memory and context-management mechanisms.
• Develop retrieval-augmented knowledge capabilities for AI agents.
• Design multi-agent workflows for complex tasks.
• Establish human-in-the-loop controls and controlled autonomy.
• Evaluate agent performance, reliability, safety, and failure modes.
• Implement security, governance, monitoring, and observability mechanisms.
• Prepare a production-ready implementation roadmap for Generative AI Agents.

Course Outline