Corporate Training — Malaysia
Agentic AI Training Malaysia
Learn how Agentic AI works and how organisations can design, build and govern AI agents and agentic workflows through practical hands-on training. Understand the shift from AI that responds to AI that reasons, plans and takes multi-step action — with human oversight built in.
Focus
Agentic AI concepts, architecture, design and governance
Duration
Flexible — tailored to your objectives
Group size
Flexible — tailored to your organisation
Delivery mode
In-house, on-site, online, or blended
Prerequisites
No advanced programming required
Related programme
Foundations
What is Agentic AI?
Agentic AI refers to AI systems that can reason, plan and take multi-step actions to achieve a goal — rather than simply responding to a single prompt. An agentic AI system can break a task into steps, retrieve information, use tools, decide when to ask a human for input, and adapt its approach based on results.
This represents a significant shift from the AI tools many organisations are already familiar with. Where a chatbot answers a question and an assistant drafts a response, an agentic system can manage an entire workflow — reading a document, checking a policy, drafting a recommendation, routing it for approval and pausing for human sign-off. It combines reasoning, planning, tool use and knowledge within governed boundaries.
Comparison
Generative AI vs Agentic AI
The terms are often used interchangeably, but they describe genuinely different capabilities. Understanding the difference is essential for deciding where each is appropriate.
Generative AI
Produces content in response to a prompt. It generates text, images or code — but it does not take action, manage workflows or use external tools. It is the underlying engine behind assistants and agents.
Agentic AI
Builds on generative AI by adding reasoning, planning, tool use and multi-step action. An agentic system can break a task into steps, retrieve information, call tools and decide when to ask a human — all within governed boundaries.
In short: Generative AI is the engine that produces content. Agentic AI is the system that uses that engine — along with reasoning, planning and tools — to accomplish complex tasks within controlled workflows.
Evolution
Chatbots vs AI agents
AI capability has evolved through distinct stages. Understanding this evolution helps organisations see where they are today and where agentic AI fits.
Chatbots
Conversational interfaces that answer questions. They respond to individual queries using predefined rules or generative AI. They answer; they do not act.
AI assistants
Tools like ChatGPT or Copilot that help with tasks — drafting, summarising, answering. They respond to requests but do not independently manage multi-step workflows.
AI agents
Software that can reason through multi-step tasks, use tools, retrieve information and operate within controlled business processes — with human oversight at the right checkpoints.
Distinction
AI automation vs Agentic AI
AI automation and Agentic AI are related but distinct. Both connect AI to workflows, but they differ in how flexibly they handle tasks. Understanding this distinction helps organisations choose the right approach for each workflow.
AI automation
Connects fixed steps together using AI within predefined workflows. The workflow is typically fixed — each step runs in a predetermined order. Good for repetitive, predictable processes.
Agentic AI
Adaptive systems that reason about the task, decide which steps to take, choose tools and adjust based on results. Good for workflows that require judgement, branching or dynamic decision-making.
Explore our AI automation training for a programme focused on building fixed automated workflows with Microsoft Power Automate.
How It Works
Agent architecture
Understanding how agents are structured helps participants design and build them effectively. The training covers the core components of agent architecture in practical business language.
Reasoning
The agent's ability to break a task into steps, evaluate options and decide what to do next. This is what makes an agent adaptive rather than scripted.
Planning
Structuring a multi-step approach to achieve a goal — deciding the order of steps, dependencies and when to pause for human input.
Tool use
Calling external tools, APIs and systems to retrieve information or take actions — within boundaries you define.
Knowledge
Connecting agents to approved knowledge sources so answers and actions are grounded in real documents rather than generated from memory.
Capability
Multi-step workflows
The defining capability of Agentic AI is the ability to manage multi-step workflows. The training covers the three core capabilities that make this possible.
Planning
The agent breaks a complex task into a sequence of steps, identifying what information it needs and what actions to take at each stage.
Tool use
The agent calls external tools and APIs to retrieve data, take actions or interact with business systems — within controlled boundaries.
Knowledge
The agent retrieves and synthesises information from approved knowledge sources, grounding its reasoning in real documents and data.
Responsibility
Human oversight
Agentic AI is powerful, but it is not infallible. Agents can hallucinate, misinterpret context and fail silently — which is why human-in-the-loop design is central to responsible deployment. The training covers how to design agents that operate within controlled business processes, not agents that operate without supervision.
Human-in-the-loop
Define checkpoints where a human reviews, approves or overrides the agent's output before it takes effect.
Approval checkpoints
Design approval flows that catch errors without becoming bottlenecks — deciding what needs human sign-off and what can proceed.
Escalation rules
Clear rules for when an agent must stop and hand off to a human — including edge cases, ambiguous inputs and error states.
Governance
Agent governance and responsible deployment
AI agents that touch real business systems need real governance. The training covers how to deploy agents responsibly — establishing ownership, accountability, approval processes and usage policies. This is not a bolt-on topic; it is woven through the programme.
Ownership and accountability
Name a human owner for every agent — someone responsible for its behaviour, outputs and continued fitness for purpose.
Data access and permissions
Grant agents access only to the data and systems they need — and document what they can and cannot read or modify.
Auditability and monitoring
Log agent actions, inputs, outputs and human decisions so every action can be traced and reviewed.
Learn more about our approach to responsible AI and how governance is built into every programme.
Applications
Business use cases
Agentic AI can support a wide range of business workflows. The use cases below illustrate how agents might be applied across different functions — with human oversight at the right checkpoints.
Document review and routing
An agent reads incoming documents, checks them against policy, drafts a recommendation and routes it for human approval — handling repetitive review while humans own the decision.
Customer support triage
An agent categorises support requests, assigns priority, suggests a first response and routes tickets — with a human approving before anything reaches the customer.
Internal knowledge retrieval
An agent answers employee questions by retrieving and synthesising content from approved knowledge sources, with citations so users can verify accuracy.
HR policy assistance
An agent answers employee questions about leave, benefits and policies using approved HR documents, escalating complex or sensitive cases to a human officer.
Sales lead qualification
An agent reviews incoming enquiries, checks them against qualification criteria, scores leads and drafts recommended responses for the sales team to review.
Management reporting
An agent gathers data from defined sources, formats it into a standard report structure and highlights key changes for an executive to review before distribution.
Practical Outcomes
Hands-on capstone project
Participants do not just learn concepts — they apply them. Each participant identifies a real workflow from their own organisation and designs an agentic workflow scoped to that process, with human-in-the-loop checkpoints and a governance checklist.
Real workflow scoping
Participants bring a real business process and scope an agentic workflow around it — not a generic demo.
Agent design
Design the agent's reasoning steps, tool use, knowledge sources and human approval checkpoints.
Governance plan
Draft a governance checklist covering ownership, data access, monitoring and escalation for the agent.
Participants leave with an agentic workflow design, a human-in-the-loop plan and a governance checklist — ready to take the next step toward implementation. For building working agent prototypes, explore our AI agent training programme.
Agentic AI Training Malaysia — Frequently Asked Questions
- Agentic AI refers to AI systems that can reason, plan and take multi-step actions to achieve a goal — rather than simply responding to a single prompt. An agentic AI system can break a task into steps, retrieve information, use tools, decide when to ask a human for input, and adapt its approach based on results. It combines reasoning, planning, tool use and knowledge within governed boundaries.
Start Your Agentic AI Journey with KadoshAI
Tell us about your organisation and your objectives. We will scope an Agentic AI training programme around your real workflows, governance requirements and team capability level. Explore our client stories to see how this approach works in practice.
You can also explore our AI automation training, corporate AI training, or our approach to responsible AI.