AI · AIC-19

AI Agents for Business

AI Agents for Business is a no-code and low-code course in building AI agents that plan and carry out multi-step work, covering ReAct, tool use, memory, multi-agent design and connecting to MCP and n8n. It suits regular AI users, IT and operations teams and team leads, and you leave able to build an agent that calls real tools and external APIs.

Updated 21 Aug 2026

From 8,910 THB / person 9,900 −10% excl. VAT 7% · group rates available

Corporate training quote Join the waiting list

PDFDownload the course outline

Duration12 hours · 2 days
FormatOnsite / live online
Next roundOn request
CertificateIncluded

Course overview

Organizations have mostly used AI in a question-and-answer style: a person asks, AI answers, and the person then does the follow-up work every time. Today AI has moved beyond an assistant waiting for commands to an AI agent that takes a goal in human language and then plans on its own, chooses its own tools, works through many steps in sequence, checks the results and adjusts until the goal is met. This capability is rapidly changing how organizations work, because tasks that once needed

a person to click through each step can now be handed to an agent from start to finish. This course is designed for professionals and teams who want to understand and actually build AI agents, focusing on business use rather than deep programming. Learners come to understand an agent's anatomy - its brain (LLM), memory, tools and planning - learn key agent patterns such as ReAct, Tool-use, Planning and Reflection, build a first agent that calls real tools and APIs, extend to multi-agent systems that

divide roles and collaborate, connect agents to real systems via MCP and platforms like n8n, and set the reliability, guardrails, human-in-the-loop, security and PDPA practices needed before putting agents into production. All content is taught through workshops on real tasks and finishes with a capstone project where learners build an end-to-end AI agent for their own organization's work. (2 days, 6 hours per day, 12 hours in total, Intermediate to Advanced level, prior generative-AI use recommended, no programming required.)

What you’ll gain

Understand what an AI agent is, how it differs from a chatbot, workflow and Custom GPT, and its autonomy levels
Understand an AI agent's anatomy: brain (LLM), memory, tools and planning
Apply key agent patterns: ReAct, Tool-use, Planning and Reflection to fit the task
Build an AI agent that calls real external tools and APIs, no-code / low-code
Design memory and knowledge so the agent recalls context and uses real organizational data (RAG for agents)
Design a multi-agent system that divides roles and collaborates (supervisor, worker, hand-off)
Connect AI agents to real systems via MCP and automation platforms like n8n
Set reliability, guardrails and human-in-the-loop practices to control quality and risk
Manage security, cost and PDPA, including AI governance for using agents in the organization
Assess which tasks suit an AI agent and plan a phased real-world rollout

Who this course is for

Those using AI in daily work who want to move from an AI assistant to a system that works on its own
IT, operations, digital and innovation teams that want to set up AI agents in the organization
Product owners, project managers and team leads who want to delegate multi-step work to AI
Customer service, sales, marketing and back-office teams with heavy repetitive processes
Business owners and executives who want to understand the potential and risks of AI agents for strategy

Prerequisites

Prior basic use of generative AI such as ChatGPT or Claude
General computer and internet skills
Understanding of your own role's workflow to use as an agent case study during training
No programming background required, though reading JSON or having called an API helps you go deeper into tools and MCP

Curriculum

Course Details

A 2-day course, 6 hours per day (12 hours in total), delivered as a hands-on workshop with a capstone project. Intermediate to Advanced level. Learners take home their own AI agent, agent and multi-agent design templates, and a reliability, security and PDPA checklist.

Day 1 Understanding and Building Your First AI Agent

Section 1: Getting to Know AI Agents

What an AI agent is and how it differs from a chatbot, workflow and Custom GPT
From Q&A to goal delegation: Perceive, Plan, Act and Observe
Agent autonomy levels and choosing the level that fits the task's risk
AI agent use cases by department and which tasks do and do not suit an agent

Section 2: Anatomy of an AI Agent

Core components: brain (LLM), memory, tools and planning
The agent loop: perceive, plan, act and check results to adjust
The role of the system prompt and goal in shaping agent behavior
Agent limits and risks: looping, wrong decisions and runaway cost

Section 3: Key Agent Patterns

ReAct (Reason + Act) alternating thinking with doing
Tool-use pattern: selecting and calling the right tool for the task
Planning pattern: breaking a big goal into sub-tasks and steps
Reflection pattern: having the agent review and improve its own work, and choosing the pattern by task and cost

Section 4: Building Your First Agent No-code / Low-code

An overview of platforms for building agents without programming, and setting goal, role and scope
Setting instructions and a stopping condition so the agent does not exceed its scope
Testing and observing the agent's planning and decisions
Workshop: build an AI agent that takes one task and plans multiple steps to a result

Section 5: Tools and Function Calling to Make the Agent Act

Tools and function calling that let the agent act, not just answer
Connecting common tools such as search, API calls, calculation and file read/write
Writing tool descriptions so the agent selects them at the right time, and handling wrong tool calls
Workshop: connect tools so the agent pulls data from an external service and completes the goal

Day 2 Multi-Agent, Real-system Integration and Governance

Section 6: Memory and Knowledge for Agents

Short-term and long-term memory and their effect on agent behavior
Having the agent use real organizational data with RAG (retrieval augmented generation)
Managing context and selecting relevant data for the agent
Reducing hallucination and having the agent cite sources

Section 7: Multi-Agent Systems and Orchestration

Why some work needs multiple agents rather than a single one
Collaboration patterns: supervisor and worker, role division and hand-off
Coordinating agents, aggregating results, and cautions on complexity and cost
Workshop: design a team of 2-3 agents that divide roles to run one process together

Section 8: Connecting AI Agents to Real Systems

Connecting agents to data and systems via MCP (Model Context Protocol)
Placing agents in automated processes with n8n and receiving events via webhook
Connecting channels such as Gmail, LINE and Slack so the agent receives tasks and returns results
Approaches for securely connecting databases and internal systems (demo)

Section 9: Reliability, Guardrails and Human-in-the-loop

Designing guardrails to limit what the agent can do
Where human approval is required (human-in-the-loop) for high-risk work
Handling errors, loops and capping step count and cost, and testing agent quality before production
Workshop: add guardrails and human approval points to the agent from the earlier workshop

Section 10: Security, Cost, PDPA and AI Governance for Agents

Agent-specific risks: over-broad tool access and prompt injection via external data
Managing permissions and credentials on least privilege
Data the agent must not access and protecting personal data under PDPA
Controlling usage cost and maintaining an audit trail and AI usage policy for agents

Section 11: Capstone Project and Real-world Adoption

Task: build an end-to-end AI agent for one of the learner's real tasks, setting goal, role, tools and memory with guardrails and human approval points
Connect the agent to a real system or channel and test end to end
Present the work and get feedback, plus a first-30-days adoption plan and extending to an organizational agent system

Schedule & training options

For individuals — public rounds

No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770

Join the waiting list Ask about rounds

For organisations — in-house / private

Tailor the content to your team’s tools and projects
Your dates, at your office or live online
Quotation with tax ID for procurement

Corporate training quote

Instructors

Samit KoyomFounder & CEO, IT Genius Engineering

Frequently asked questions

How does an AI agent differ from a chatbot and an automated workflow?

A chatbot answers one message at a time as the user asks. An automated workflow follows steps that a person defined in advance, every time. An AI agent takes a goal, plans its own approach, chooses which tools to use, carries out several steps, checks the results and adjusts when something goes wrong. That suits work whose steps vary with the situation, but you must set its level of autonomy and approval points according to the risk.

What is the ReAct pattern for AI agents, and how does it work?

ReAct, short for reason and act, has an agent alternate between thinking and doing. It reasons about what to do next, calls a tool such as web search, a file reader or an API, observes the result, and uses that to decide the next step, repeating until the task is done. Answers are grounded in real data and you can trace how the agent decided, but you should cap the number of loops to stop it running endlessly.

How does a multi-agent system with a supervisor and workers operate?

A supervisor agent takes the goal, breaks it into subtasks and hands each off to a worker agent that specialises in one area, for example one researches, another writes the report and a third checks accuracy. When the workers return their results, the supervisor combines and reviews them and decides whether to move on or ask for revisions. Each agent has its own focused instructions and tools, which is easier to maintain and debug than a single agent doing everything.

Who is the AI Agents for Business course for, and what should I know beforehand?

It suits regular AI users who want to move from an AI assistant to a system that works on its own, IT, operations and innovation teams setting up agents, team leads who want to delegate multi-step work, and executives who want to understand the potential and risks. You should have used ChatGPT or Claude. Programming is not required, though reading JSON or having called an API helps with tools and MCP.

What will I be able to do after the AI Agents for Business course?

You will be able to judge which work suits an AI agent, build no-code or low-code agents that call real tools and external APIs, apply patterns such as ReAct, planning and reflection, give agents memory and access to organisational knowledge, design multi-agent systems, connect them to business systems through MCP and n8n, and put guardrails and human review in place. Participants who complete the training receive a Certificate of Completion with its own number, which can be verified online.

Can the AI Agents for Business course be run in-house for our team?

Yes. It can be run in-house for your team, with examples adapted to your own work, such as designing agents around your departments' real processes and setting human approval points in line with your organisation's policies. You can request a quotation from this page, or contact us on LINE @itgenius or by phone on 02-570-8449.