AI Agents for Business is a 12-hour training course by IT Genius Institute. 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…
Training schedule
No public rounds are open right now — register your interest and we will contact you when the next round opens, or request an in-house session for your team.
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.)
Objectives
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
Frequently asked questions
Who is AI Agents for Business for, and what background is needed?
Built 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 Background you should have: Prior basic use of generative AI such as ChatGPT or Claude · General computer and internet skills Not sure the fit is right? Talk to our team on LINE @itgenius or call 02-570-8449.
How much does AI Agents for Business cost and how long does it run?
THB 9,900 (currently THB 8,910 on promotion). The course runs 12 hours. The fee covers course materials, lunch and refreshments throughout. Pay by bank transfer to the company account, confirm it on our payment page, and we can issue the receipt or tax invoice in your company's name.
Do I get a certificate?
Yes. Everyone who completes the course receives a Certificate of Completion from IT Genius Institute. Each certificate carries its own number, and anyone holding that number can verify it online on our certificate page, so you can add it to your portfolio or pass it to HR as evidence of training.
Where does the training take place, and is there an online option?
You can attend onsite at IT Genius Institute or arrange to join online, and we also run it as a private in-house session for your team. Ask about dates and venues on LINE @itgenius or call 02-570-8449.
What if I fall behind or miss a session — can I retake it?
Yes. You may retake the same course free of charge in a later round, under the institute's conditions. Tell our team which course and round you attended, and we will check it and offer you the rounds that still have seats. Ask us on LINE @itgenius or call 02-570-8449.
How do I enrol, or request a quotation for my company?
Enrol online with the registration form on this page. You can register several attendees at once and enter your tax ID and billing address for the tax invoice. Or request a company quotation straight from the quote button. For anything else call 02-570-8449 or reach us on LINE @itgenius.
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5,310THB5,900 THB
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