AI Agents in Practice – Automation of Working with Code, Text, and Browser

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

Designing task-focused agents - You will learn how to set up AI agents for multi-step work so they can plan actions, execute tasks, and verify results instead of behaving like a standard chatbot.

Automating content and file work - You will see how to speed up editing, SEO content preparation, document analysis, and bulk file operations so you can cut down manual fixes and tedious file handling.

Faster work with code - You will practice using an agent to generate scripts, review existing code, find bugs, and refactor solutions, helping you deliver technical tasks more quickly and reliably.

Building practical workflows - You will learn how to connect multiple steps into one process with approval checkpoints, making it easier to automate reports, data monitoring, and routine daily tasks.

Tool integrations with MCP - You will learn how to connect agents to apps, databases, files, and other systems through MCP so AI can safely perform specific operations in your actual work environment.

Browser task automation - You will master browser control with Playwright MCP to click elements, fill forms, extract data, take screenshots, and test deployments without doing each step manually.

Creating repeatable procedures - You will learn how to turn company procedures into reusable skills and agent instructions so your team can work in a consistent way and reuse proven execution patterns.

Better control of execution - You will learn to separate planning from execution, define specs and acceptance criteria, and split work across subagents to reduce errors, rework, and misunderstandings.

Training programme

1. Introduction to AI agents

what an AI agent is and how it differs from a classic chatbot,
independent execution of tasks by agents AI,
agent operation scheme: planning, execution, control and improvement of the result,
delegating complex tasks and multi-step work,
creating effective instructions for agents,
defining the goal, context, constraints and the expected result,
approval of the action plan before executing the task,
project instruction files, e.g. AGENTS.md,
creating permanent rules concerning the project, the company and the client.

2. AI Agents in working with text and files

creation and editing of content with the use of AI agents AI,
preparation of briefs, articles, descriptions and SEO content,
generation of meta title, meta description and content structures,
analysis and modification of many documents simultaneously,
bulk operations on files,
organizing, renaming and classification of documents,
conversion of files between different formats,
automatic editing and updating of many documents,
analysis of data located in files,
generation of reports, summaries and recommendations.

3. AI Agents at work with code

code generation based on a description in natural language,
creating simple scriptós automating daily work,
analysis of existing code,
finding errors and proposing fixes,
expansion and refactoring of code,
creating technical documentation,
performing multi-step programming tasks by an agent,
control of resultós and testing of the generated solution.

4. Task and Process Automation

automatic execution of repetitive tasks,
creation of cyclical reportós,
monitoring of data, fileós and selected processóes,
automatic processing of new information,
designing workflow with the use of agentós AI,
combining several stepós into one automatic process,
defining momentós requiring user acceptance.

5. AI agent integrations with external tools – MCP

introduction to the Model Context Protocol,
the role of MCP in the work of AI agents,
connecting agents to external applications and data sources,
providing the agent with tools to perform specific operations,
examples of integrations with systems, files, databases and applications,
security and control of the agent's permissions.

6. Playwright MCP – browser work automation

browser control by an AI agent,
automatic opening of pages and navigation between subpages,
clicking elementsów and handling forms,
entering and retrieving data from pages,
automation of repetitive activities performed in online panels,
testing forms and linksów,
verification of website operation andnternet,
testing the view desktopowy and mobile,
automatic taking screenshots,
audit of deployments and control of the correctness of changes,
creating a report of the tests carried outów.

7. Skills – creating repeatable procedures for agents

what skills are and when it is worth using them,
conversion of company procedures into instructions performed by AI,
building repeatedly used procedures,
standardization of the way of performing tasks,
creating skillós for specific departmentsós and processesós,
combining several skillós into a more elaborate workflow.

8. Subagents – division of complex tasks

what subagents are,
delegating part of the task to specialized agents,
division of work according to competencies,
an agent responsible for research, content, code, tests and quality control,
coordination of the results of the work of many agents,
building multi-agent processes.

9. OpenSpec – specification before task execution

why it is worth separating planning from execution,
creating the solution specification before starting work,
defining requirements and acceptance criteria,
verification of the scope before implementation,
accepting and modifying the plan by the user,
reducing errors and misunderstandings in larger projects,
moving from the approved specification to execution.

10. Practical workshops – building your own workflow with an AI agent

selection of the process for automation,
preparation of instructions and rules for the agent,
planning the subsequent stagés of operation,
adding tools and integrations,
automation of work with text, files or a browser,
testing the agent's operation,
analysis of errors and improvement of the workflow,
preparation of a solution possible to be used in everyday work.

What are the prerequisites for participating in the training?

Confident computer use - You should be comfortable using a computer, navigating folders, opening files, and working in a browser so you can focus on AI agents rather than basic computer skills.

Basics of text and file work - You should know how to edit text, move information between documents, and recognize common file formats, because the training covers automating these everyday tasks.

Basic understanding of workflows - You should be able to describe the steps in your own work process, spot manual activities, and identify approval points so you can design useful AI-driven workflows.

Basic technical logic - You do not need to be a developer, but you should understand simple step-by-step logic, conditions, and outcomes, as the training includes automation, testing, and code.