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Develop Multi-Agent Systems with OpenAI Agent SDK

Course Information

  • Sessions 2 days
  • Duration 15 hrs
  • Level Beginner
  • Assessment NA

Venue

Kuala Lumpur: G-3A-02, Corporate Office Suite, KL Gateway, No. 2, Jalan Kerinchi, Gerbang Kerinchi Lestari, 59200 Kuala Lumpur, Malaysia

Learning Outcomes

By the end of the course, learners will be able to:

  • LO1: Explain the OpenAI Agents SDK, including agents, instructions, tools, handoffs, guardrails and tracing.
  • LO2: Build a RAG agent that answers questions from your own documents.
  • LO3: Orchestrate multiple agents with triage and specialist roles, handoffs and tracing.
  • LO4: Connect agents to external tools and data sources using the Model Context Protocol (MCP).
  • LO5: Fine-tune LLM models for a domain and evaluate the results.

Course Brochure

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Prerequisite

This course is for beginners. No prerequisite is required.

Certification

  • Participants who achieve at least 75% attendance will receive a Certificate of Attendance from Tertiary Courses Malaysia.

Develop Multi-Agent Systems with OpenAI Agent SDK

Course Code: C765

6 Review(s) | Add Your Review

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What's This Course About

Develop Multi-Agent Systems with OpenAI Agent SDK teaches you how to design, build and deploy multi-agent AI systems using OpenAI's Agents SDK. Multi-agent systems split complex work across specialised agents that use tools, retrieve knowledge, hand off tasks to one another and work under guardrails, enabling applications that are more capable and reliable than a single chatbot. The course is designed for developers, AI engineers, data scientists and technical professionals with Python experience who want to build production-ready agentic applications.

You will start with an introduction to the OpenAI Agents SDK, covering agents, instructions, tools, handoffs, guardrails, sessions and tracing, and building your first agents in Python. Next you will build a retrieval-augmented generation (RAG) agent that answers questions accurately from your own documents using file search and vector stores. You will then orchestrate multiple agents, designing triage and specialist agents, managing handoffs and parallel work, and tracing runs to debug and improve agent behaviour.

The course continues with the Model Context Protocol (MCP), connecting your agents to external tools and data sources through MCP servers, and closes with fine-tuning LLM models, learning when and how to customise a model for your domain and evaluate the results. Throughout the course you will also cover safety, guardrails, cost control and deployment practices. Every topic is hands-on. By the end, you will be able to develop, evaluate and deploy multi-agent systems with the OpenAI Agents SDK confidently.

Why Take This Course?

  • Hands-On Learning Learn by doing. Every topic is practised hands-on, so you leave with skills you can use at work straight away.
  • Live Coaching Experienced instructors coach you live, answer your questions and guide you step by step.
  • Free Post-Course Consultation Stuck after class? Message your trainer any time for guidance as you apply what you have learned.
  • Always-Updated Materials Materials are refreshed to stay relevant. Log in to our LMS any time to get the latest version.
HRD Corp Claimable

🏦 HRD Corp Claimable

This course is HRD Corp claimable for eligible employers. Grant approval and reimbursement remain subject to HRD Corp requirements, the employer’s available levy and the approved grant details. How the HRD Corp process works →

Course Fee

MYR2,200.00
+ 8% SST

Course Date

Course Time

Course fees are shown before SST. An 8% SST is included in the estimated total during registration.

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Disclaimer: The course dates displayed on our website are tentative and subject to trainer availability. We will confirm the final date after checking with the trainer. You are also welcome to email us your preferred date at sales@tertiarycourses.com.my, and we will do our best to coordinate with the trainer's schedule.

Post-Course Support

  • We may provide consultation related to the subject matter after the course.
  • Please email your queries to sales@tertiarycourses.com.my and we will forward your queries to the subject matter experts and get back to you as soon as possible.

Cancellation & Reschedule Policy

  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
  • Note: the venue of the training is subject to changes due to class size and availability of the classroom. The minimum class size to start a class is 3 Pax.

Course Details

Course Details

What You'll Learn

Topic 1: Introduction to OpenAI Agent Development Kit

Topic 2: Building RAG Agent

Topic 3: Orchestrating Multiple Agents

Topic 4: Model context protocol (MCP)

Topic 5: Fine Tuning LLM Models

Course Info

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Minimum Software/Hardware Requirement

Software:

You will need the following for this course:

Hardware: Windows PC or Mac laptop

How to Apply HRD Corp Funding

This course is HRD Corp claimable for employers registered with HRD Corp, under the HRD Corp Claimable Courses scheme (SBL-Khas). Your company applies for the grant before the class; we submit the training provider claim after it.

  1. Check your levy balance - your company must be registered with HRD Corp and have enough levy balance to cover the course fee.
  2. Request a quotation - contact us for the quotation, course content and trainer profile; you attach these to the grant application.
  3. Apply for the grant in eTRiS before the class - log in to your employer eTRiS account, go to Application > Grant > Apply Grant, choose the scheme HRD Corp Claimable Courses (SBL-Khas), select us as the training provider and this course, add your trainees, attach the quotation, course content and trainer profile, and submit.
  4. Submit in time - for public classes, submit at least 3 calendar days before the class (15 June to 31 December 2026) and at least 14 days before from 1 January 2027; in-house classes need 14 days. The training must start within 90 days of that advance submission period.
  5. Send us the grant approval - once HRD Corp approves the application, email us the grant ID so we can confirm your seats.
  6. Attend the class - trainees sign the daily attendance (T3) form.
  7. Claim after the class - we submit the training provider claim in eTRiS with the attendance and invoice; you then complete the employer claim and sign the JD/14 form (manager level or above). Claims must be submitted within 6 months of the training.

More details: our HRD Corp Claimable Courses page and the HRD Corp employer guidelines.

Job Roles

Job Roles

  • NLP (Natural Language Processing) Engineer
  • Machine Learning Engineer
  • AI Application Developer
  • Chatbot Developer
  • AI Research Scientist
  • AI Product Manager
  • Data Scientist specializing in text data
  • Conversational AI Designer
  • AI Integration Specialist
  • Language Model Trainer
  • Content Personalization Engineer
  • AI Platform Engineer
  • Cognitive Computing Specialist
  • AI Technical Consultant
  • AI Software Architect

Trainers

Trainers

Multi-agent systems, retrieval-augmented generation and tool integration are the building blocks Dr. Alfred Ang works with every week. Dr. Ang is a full-stack AI developer, Agentic AI solution architect and corporate trainer, and serves as CTO of Tertiary Infotech Academy Pte Ltd, the Singapore training organisation behind Tertiary Courses. Dr. Ang's RAG work includes n8n-ragdemo, in which uploaded PDFs feed a vector store that a Telegram AI agent queries, and n8n-ragdemo2, a customer-service RAG chatbot over a library of course brochures. Dr. Ang has also built n8n-hrportal, which wraps an OpenAI HR chatbot in input and output guardrails with human-in-the-loop approval, and digitalhuman, a voice-cloned avatar trained on custom content through RAG. On the orchestration side, an ERP prototype coordinates several AI agents for purchase orders, invoice matching and replenishment, hermes-video-pipeline lets an agent produce YouTube videos end-to-end, and AI-LMS-TMS embeds agent automation into a full training management system. Much of this was developed with Claude Code and the Claude Agent SDK, using Model Context Protocol servers to connect agents to tools and data. More than 300 public repositories at github.com/alfredang show the practical side of agent engineering: designing roles, passing context safely, handling failures and deploying agents that keep working once real users arrive. Dr. Ang also pays close attention to cost, latency and observability, because a multi-agent system that is slow, expensive or impossible to debug rarely survives contact with real users.

Develop Multi-Agent Systems with OpenAI Agent SDK turns that experience into hands-on practice. The course opens with an introduction to the OpenAI agent development kit, covering agents, tools, handoffs and guardrails, and setting up a working Python project. Learners then build a RAG agent, chunking and embedding documents, storing them in a vector database and grounding answers in retrieved sources. The third topic orchestrates multiple agents, assigning specialist roles, routing requests and passing context between agents to complete multi-step tasks. The fourth topic introduces the Model Context Protocol, showing how MCP servers expose tools and data to agents in a standard, reusable way. The final topic covers fine-tuning LLM models, including when fine-tuning beats prompting or retrieval, how to prepare training data and how to evaluate the result. Dr. Ang codes live and explains design choices as they arise, drawing on production agents to show common pitfalls such as runaway tool calls, context overflow and hallucinated citations, and how to guard against them. Participants build and extend their own multi-agent application throughout the course, leaving with working code they can adapt for business use. Developers, data scientists and technical architects with basic Python skills will gain a practical, end-to-end view of agent engineering. Learners also explore tracing, logging and evaluation techniques for agents, so they can see exactly which agent made each decision and measure quality as the system grows. To explore the RAG chatbots and agent systems behind these lessons, visit Dr. Ang's portfolio at https://github.com/alfredang.

Review

Customer Reviews (6)

Would attend again Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Very engaging session. The trainer patiently answered all our questions and gave useful tips. (Posted on 27/05/2026)
Worth every cent Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Very informative and interactive. The small class size meant we got a lot of personal attention. (Posted on 10/02/2026)
Practical and relevant Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Good hands-on training with plenty of examples. I feel much more confident applying these skills now. (Posted on 15/01/2026)
Superb training Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 19/06/2025)
Hands-on and practical Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 02/02/2025)

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