Build and Deploy Agentic AI Apps with CrewAI, Autogen, ADK and Streamlit
About This Course
This course provides learners with a comprehensive understanding of how to build and deploy cutting-edge Agentic AI applications using CrewAI, Autogen, ADK, and Streamlit. Participants will gain practical skills in designing multi-agent workflows, integrating these frameworks for intelligent and context-aware AI applications. The course covers best practices for deploying these applications on cloud-based platforms and introduces retrieval-augmented generation (RAG) techniques to boost application effectiveness. Learners will also explore essential strategies for deploying guardrails to ensure that outputs are safe, aligned, and compliant with ethical AI practices.
In today’s rapidly evolving AI landscape, these skills are highly relevant and sought-after. Proficiency in building multi-agent AI systems and deploying them effectively on popular frameworks opens up exciting career paths in AI engineering, data-driven product development, and machine learning infrastructure. Moreover, the inclusion of model alignment and guardrail strategies prepares learners to create AI applications that meet high standards of safety and responsibility, a key differentiator in modern AI-driven businesses. Completing this course can significantly enhance job prospects and support transitions into more advanced technical roles.
This course is designed for beginner to intermediate learners who have some experience with AI concepts and basic coding, and are looking to expand their knowledge into multi-agent workflows and advanced AI deployment.
What You'll Learn
LO1: Design multi-agent integration workflows using CrewAI, Autogen, and ADK for intelligent AI application development.
LO2: Develop and deploy Agentic AI and RAG applications on Streamlit and cloud-based platforms.
LO3: Deploy guardrails to ensure safe and aligned outputs in Agentic AI applications.
Course Outline:
LU1: Agentic AI App Development with Agentic AI Frameworks
T1:Introduction to Agentic AI frameworks - CrewAI, Augtogen and OpenAI ADK
T2 Overview of multi-agent workflows
T3: Agentic AI workflow design
T4: Build Agentic AI Applications with Agentic AI frameworks
T5: Development with AI IDE - GitHub Copilot, Cursor, Trae or Windsurf
LU2: Agentic AI and RAG Deployment on Streamlit Cloud
T1:Concepts behind context augmentation techniques, such as knowledge graphs
T2: Approaches to deploy models for inference using cloud platforms
T3: Identify issues in Agentic AI pipeline by analysing and interpreting error logs
T4: Deploy RAG an Agentic AI Apps on Streamlit Cloud or Railway
LU3: Model Alignment and Guardrails
T1:Safeguarding Large Language Models (LLMs) with guardrails for content generation
T2:Model alignment for safety and alignment
T3:Deploy guardrails on Agentic AI applications.
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
Target Age Group: 21 to 65 years old