Claude Certified Associate - Foundations (CCAO-F) Exam Prep

Shipping agentic AI applications that real users can try in a browser is central to the work of Dr. Alfred Ang, a full-stack AI developer, Agentic AI solution architect and corporate trainer who serves as CTO of Tertiary Infotech Academy Pte Ltd, the Singapore training organisation behind Tertiary Courses. Streamlit features prominently in Dr. Ang's portfolio: a courseware generation platform built with the Claude Agent SDK and Streamlit that automates the creation of training documents, a course-proposal generator powered by Claude, a document-generation app for training-partner procedures, and data-mining-ml-labs, a set of interactive Streamlit labs for machine learning. Dr. Ang has also built RAG systems, from n8n-ragdemo, in which a Gemini-powered Telegram agent answers questions over uploaded PDFs, to n8n-ragdemo2, a customer-service chatbot over a library of course brochures. Guardrails and alignment are treated as first-class concerns, as in n8n-hrportal, whose HR chatbot is protected by input and output guardrails and human-in-the-loop approvals. Multi-agent orchestration appears in an ERP prototype that coordinates AI agents for procurement, invoicing and replenishment, and in AI-LMS-TMS, a training management system with built-in agent automation. More than 300 public repositories at github.com/alfredang demonstrate the full journey from framework experiment to deployed, guarded application. Dr. Ang has also compared several agent frameworks in practice, learning where each one shines, where it struggles and how to keep code portable as the ecosystem evolves. That comparative, hands-on perspective shapes the structure of the whole course. Lessons from failed experiments are shared as openly as the successes.

Build Multi Agent Systems with ADK, Streamlit, Autogen and CrewAI gives learners a practical tour of today's leading agent frameworks. The first topic, agentic AI app development with agentic AI frameworks, compares Google ADK, Microsoft Autogen and CrewAI, and has learners build agents with defined roles, tools and collaboration patterns in each, so they understand the strengths and trade-offs of every approach. The second topic, agentic AI and RAG deployment on Streamlit Cloud, adds retrieval over documents, wraps the agents in a Streamlit interface and deploys the finished application so it can be shared with colleagues or clients. The final topic, model alignment and guardrails, covers prompt-injection defences, output validation, content filtering, human-in-the-loop checkpoints and techniques for keeping agents on task and within policy. Dr. Ang codes live and explains design decisions in context, drawing on the Streamlit apps and guarded chatbots already in production to show what tends to break when agents meet real users. Participants build and deploy their own multi-agent application during the course and leave with code they can extend. Developers, data scientists and solution architects with basic Python skills will gain a clear, framework-neutral understanding of how to design, ship and safeguard multi-agent systems. Learners also explore how to monitor deployed agents, manage API keys and secrets safely on Streamlit Cloud, and estimate the running costs of different model choices, so their applications remain secure and affordable after launch. To explore the Streamlit apps, RAG chatbots and agent projects behind this course, visit Dr. Ang's portfolio at https://github.com/alfredang.