Claude AI – AI Agents and Workflows Automating Work

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?

Differentiate AI roles - You will clearly distinguish a chatbot, an AI assistant, and an AI agent, so you can match the right tool to the task and avoid deploying solutions that do not fit your process.

Design AI workflows - You will turn a real business process into a single-step or multi-step AI workflow, from inputs and decision points to output validation and a usable result for your team.

Organize knowledge in Claude - You will learn how to structure Claude Projects, manage documents, control context, and build project memory so your agent can work from current and reliable company knowledge.

Build no-code tools - You will create practical solutions in Claude Artifacts, including AI forms, dashboards, calculators, and document generators that can support your team's daily work right away.

Write stronger prompts - You will learn how to define agent roles, goals, constraints, and operating instructions to run multi-step processes, automate analysis, and gain tighter control over model behavior.

Connect Claude to tools - You will see how to use MCP to securely connect Claude with company documents, knowledge repositories, SharePoint, or Google Drive and give agents the context they need.

Automate document analysis - You will practice working with PDFs, contracts, reports, and technical documentation, from data extraction and classification to recommendations and a final report ready to use.

Deploy AI safely - You will prepare an implementation plan for an AI agent that covers data protection, access rights, auditing, monitoring, and human-in-the-loop controls for safe automation.

Training programme

1. Introduction to AI Agents and Claude

what AI agents are:
- differences between a chatbot, an AI assistant and an AI agent,
- AI taking actions,
- workflow based on language models,
- examples of AI agent applications in business,
Claude ecosystem:
- Claude Desktop,
- Claude Projects,
- Claude Artifacts,
- Claude API,
- Claude as a platform for building workflow.

2. AI workflow design

fundamentals of process design:
- mapping business processes,
- identifying tasks for automation,
- single-stage and multi-stage workflows,
- combining AI activities with the organization's processes,
creating workflow architecture:
- input data,
- information processing,
- decision-making,
- generating results,
- validation of results.

3. Claude Projects as an agent's work environment

knowledge organization:
- creating projects,
- document repositories,
- context management,
- building project memory,
working with organizational knowledge:
- procedures,
- instructions,
- project documentation,
- the company's knowledge base,
creating specialized agents:
- analytical agent,
- documentation agent,
- project agent,
- team support agent.

4. Claude Artifacts and building AI tools

creating applications without programming:
- interactive tools,
- AI forms,
- dashboards,
- document generators,
building custom solutions:
- calculators,
- reporting systems,
- tools supporting business processes,
- tools supporting data analysis,
iterative application development:
- testing,
- modifying functionality,
- workflow expansion,
- version management.

5. Advanced prompting of agents

system Prompt Engineering:
- defining agent roles,
- operational constraints,
- goals and operational instructions,
- agent behavior control,
designing multi-step processes:
- Chain of Tasks,
- step-by-step workflow,
- analysis automation,
- decision automation,
creating process templates:
- reporting workflows,
- analytical workflows,
- sales workflows,
- administrative workflows.

6. MCP and Claude integration with tools

introduction to MCP (Model Context Protocol):
- MCP architecture,
- integration with external systems,
- secure sharing of data with AI models,
integration examples:
- company documents,
- knowledge repositories,
- SharePoint,
- Google Drive,
- project systems,
creating the agents' work environment:
- access to organizational knowledge,
- data updates,
- management of information sources,
- permission control.

7. AI Agents for Document Analysis

automation of work with documentation:
- PDF analysis,
- contract analysis,
- report analysis,
- technical documentation analysis,
expert agents:
- legal agent,
- financial agent,
- HR agent,
- project agent,
multi-stage document analysis:
- data extraction,
- information classification,
- recommendation generation,
- creation of final reports.

8. AI Agents for Teamwork

support for project work:
- task management,
- creating summaries,
- follow-ups after meetings,
- project knowledge management,
communication automation:
- creating responses,
- generating documents,
- preparing presentations,
- handling administrative processes,
human + AI collaboration:
- Human-in-the-loop,
- validation of results,
- delegating tasks to agents,
- supervision of processes.

9. Multi-Agent Workflow

designing multi-agent systems:
- division of roles,
- specialization of agents,
- exchange of information between agents,
- coordination of activities,
business scenarios:
- analysis and reporting,
- customer service,
- project management,
- documentation management,
orchestration of AI processes:
- management of information flow,
- task queueing,
- decision-making,
- problem escalation.

10. Security, governance and implementations

secure use of AI agents:
- data protection,
- confidential data,
- access permissions,
- risk management,
AI Governance:
- quality control of responses,
- audit of agents' operation,
- process monitoring,
- responsibility management,
Final workshop:
- designing your own AI agent:
  - business process analysis,
  - workflow construction,
  - creating instructions for the agent,
  - testing the operation,
- building a complete solution:
  - agent + documents + workflow,
  - automation of the business process,
  - validation of results,
  - implementation plan in the organization.

What are the prerequisites for participating in the training?

Computer basics - You should be comfortable working on a computer and in a web browser, using online documents, and moving efficiently between applications used in your daily work.

Process awareness - You should understand at least one process from your role or company so you can map tasks, identify stages, and assess what can realistically be automated.

Document handling - You should know how to work with documents such as procedures, instructions, reports, or contracts, because you will analyze and organize this kind of material.

Basic AI usage - You should have basic experience using AI tools and writing simple prompts, so you can move more easily into designing agents and building more advanced workflows.