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Online-Development Programme: AI Agents for Business, Automation, and Creativity

IT Personal Effectiveness Personal Development
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  Online-Development Programme: AI Agents for Business, Automation, and Creativity. Date: 25 June2026. Time: 17:00 19:00. Language: Ukrainian. Place: Online Zoom. The programme is built around the following principle: explanation – examples – template – practice – result. Theory is kept to the minimum necessary, while the main focus is on the practical creation of agents and applying this logic to real work-related tasks.  EBA Management Development Centre team, together with Mykhailo Patsan, invite you to participate in the Online-Development Programme: AI Agents for Business, Automation, and Creativity.  Target audience: everybody who is in the process of creating or working with AI-agents. PROGRAMME Module 1. What AI Agents Are and How They Work - June 25, 17:00–19:00  Objective  To give participants a clear understanding of what an AI agent is, how it differs from a regular chatbot or prompt, what its architecture looks like, and where agents can deliver real business value.  Topics for discussion: What an AI agent is, explained in simple terms.  The difference between a chatbot, an assistant, automation, and an agent.  Why an agent is a system with a role, memory, tools, action logic, and a controlled outcome.  Which tasks should be delegated to agents, and which are better left to a person or classical automation.  Typical risks of working with agents: hallucinations, unclear data, weak quality criteria, and lack of control.  AI Agent Architecture  Tools  Tool  What We Use It For  Claude  Reasoning, analysis, texts, documents, methodologies, business logic, and agent workflows.  Codex  Code, scripts, integrations, technical agents, automation, and working with repositories.  OpenClaw  Building agent architecture, launching multiple agents, task routing, and systematic agent workflows.  Session Practice: Each participant selects one real task from their own work.  They describe it using a simple framework: input data, performer, expected result, risks, and quality criteria.  Together, we determine whether this task is suitable for an AI agent.  Module 2. Creating AI Agents for Functional Areas - July 2, 17:00–19:00  Objective  To teach participants how to design agents for specific business functions: sales, marketing, HR, finance, operations, support, legal, analytics, and management.  Methodology for Selecting Tasks for Agents: A task is well suited for an AI agent if it is repetitive, has a clear input, a clear output, quality criteria, and can be reviewed by a human at the final stage.  The task is performed regularly.  There is clear input data: text, documents, spreadsheets, correspondence, CRM, call recordings, and tasks.  There is an expected result: a report, conclusion, table, action, recommendation, or file.  There are rules or examples of high-quality execution.  The task takes a lot of the team’s time.  The result can be reviewed before use.  Session Practice  Each participant creates a concept for their own functional agent.  The group reviews 2–3 examples of agents for different areas.  Participants develop the first version of a prompt or technical specification for their own agent.  Examples of Agents  Example  What It Does  Sales Agent  Analyzes managers’ calls, identifies strengths and weaknesses, records the outcome of the conversation, provides recommendations, and prepares a report for the manager.  HR Agent  Analyzes candidates’ CVs, compares them with job requirements, determines relevance, and prepares a short conclusion.  Legal Analysis Agent  Reads a contract, identifies risks for the parties, explains them, and suggests how to reduce those risks.  Module 3. Creative AI Agents and Launching Your Own Agent - July 9, 17:00–19:00  Objective  To show how to create creative agents for content, ideas, visuals, presentations, scripts, advertising campaigns, and communications. At the final stage, participants will build their own agent and test it on a real task.  What a Creative AI Agent Is: It understands the brand, audience, product, and tone of communication.  It generates ideas but does not stop at raw drafts.  It creates the structure of content, presentations, videos, or advertising campaigns.  It adapts messages for different channels and formats.  It checks the result against the task, style, and quality criteria.  Session Practice  Participants create their own agent based on the task they selected during the first session. The final agent structure must be clear enough to be transferred to Claude, Codex, or OpenClaw.  Block  Result  Agent Name  Ready-made name.  Role  Clearly described function.  Task  What the agent solves.  Input Data  What needs to be provided to the agent.  Workflow Algorithm  Step-by-step logic.  Tools  Claude, Codex, OpenClaw, or others.  Result Format  Table, report, file, text, presentation, code.  Quality Check  How a person checks the agent’s work.  Next Steps  How to implement the agent in a real process.  After completing the programme, participants will get certificattes.Speakers. Mykhailo Patsan. Investor, enterpreneur, expert in the sphere of international financial markets and cryptocurrency, co-founder of Web3 University Learn to Earn Global. You can send a question to:. Contact person:. Iryna Shevchuk. E-mail [email protected]. Contact Phone. 067 240 90 90.

About the Programme

The programme is built around the following principle: explanation – examples – template – practice – result. Theory is kept to the minimum necessary, while the main focus is on the practical creation of agents and applying this logic to real work-related tasks.

EBA Management Development Centre team, together with Mykhailo Patsan, invite you to participate in the Online-Development Programme: AI Agents for Business, Automation, and Creativity.

Target audience: everybody who is in the process of creating or working with AI-agents.

PROGRAMME

Module 1. What AI Agents Are and How They WorkJune 25, 17:00–19:00

Objective

To give participants a clear understanding of what an AI agent is, how it differs from a regular chatbot or prompt, what its architecture looks like, and where agents can deliver real business value.

Topics for discussion:

  • What an AI agent is, explained in simple terms.
  • The difference between a chatbot, an assistant, automation, and an agent.
  • Why an agent is a system with a role, memory, tools, action logic, and a controlled outcome.
  • Which tasks should be delegated to agents, and which are better left to a person or classical automation.
  • Typical risks of working with agents: hallucinations, unclear data, weak quality criteria, and lack of control.
  • AI Agent Architecture

Tools

Tool

What We Use It For

Claude

Reasoning, analysis, texts, documents, methodologies, business logic, and agent workflows.

Codex

Code, scripts, integrations, technical agents, automation, and working with repositories.

OpenClaw

Building agent architecture, launching multiple agents, task routing, and systematic agent workflows.

Session Practice:

  • Each participant selects one real task from their own work.
  • They describe it using a simple framework: input data, performer, expected result, risks, and quality criteria.
  • Together, we determine whether this task is suitable for an AI agent.

Module 2. Creating AI Agents for Functional Areas – July 2, 17:00–19:00

Objective

To teach participants how to design agents for specific business functions: sales, marketing, HR, finance, operations, support, legal, analytics, and management.

Methodology for Selecting Tasks for Agents:

A task is well suited for an AI agent if it is repetitive, has a clear input, a clear output, quality criteria, and can be reviewed by a human at the final stage.

The task is performed regularly.

  • There is clear input data: text, documents, spreadsheets, correspondence, CRM, call recordings, and tasks.
  • There is an expected result: a report, conclusion, table, action, recommendation, or file.
  • There are rules or examples of high-quality execution.
  • The task takes a lot of the team’s time.
  • The result can be reviewed before use.

Session Practice

  • Each participant creates a concept for their own functional agent.
  • The group reviews 2–3 examples of agents for different areas.
  • Participants develop the first version of a prompt or technical specification for their own agent.

Examples of Agents

Example

What It Does

Sales Agent

Analyzes managers’ calls, identifies strengths and weaknesses, records the outcome of the conversation, provides recommendations, and prepares a report for the manager.

HR Agent

Analyzes candidates’ CVs, compares them with job requirements, determines relevance, and prepares a short conclusion.

Legal Analysis Agent

Reads a contract, identifies risks for the parties, explains them, and suggests how to reduce those risks.

Module 3. Creative AI Agents and Launching Your Own Agent – July 9, 17:00–19:00

Objective

To show how to create creative agents for content, ideas, visuals, presentations, scripts, advertising campaigns, and communications. At the final stage, participants will build their own agent and test it on a real task.

What a Creative AI Agent Is:

  • It understands the brand, audience, product, and tone of communication.
  • It generates ideas but does not stop at raw drafts.
  • It creates the structure of content, presentations, videos, or advertising campaigns.
  • It adapts messages for different channels and formats.
  • It checks the result against the task, style, and quality criteria.

Session Practice

Participants create their own agent based on the task they selected during the first session. The final agent structure must be clear enough to be transferred to Claude, Codex, or OpenClaw.

Block

Result

Agent Name

Ready-made name.

Role

Clearly described function.

Task

What the agent solves.

Input Data

What needs to be provided to the agent.

Workflow Algorithm

Step-by-step logic.

Tools

Claude, Codex, OpenClaw, or others.

Result Format

Table, report, file, text, presentation, code.

Quality Check

How a person checks the agent’s work.

Next Steps

How to implement the agent in a real process.

After completing the programme, participants will get certificattes.

Trainers

1 / 1
Mykhailo Patsan
Investor, enterpreneur, expert in the sphere of international financial markets and cryptocurrency, co-founder of Web3 University Learn to Earn Global
Mykhailo Patsan

You can send a question to:

Contact person:

Iryna Shevchuk

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