Artificial Intelligence
Agentic AI & MCP
Artificial intelligence is evolving from a passive tool that merely answers questions into 'agentic' systems capable of planning, making decisions, and executing tasks end-to-end. Thanks to standards such as Model Context Protocol (MCP) developed by Anthropic, these agents can now connect securely and consistently to file systems, databases, APIs, and business applications—paving the way for revolutionary use cases in areas such as autonomous software development, automated customer service, data analysis, and business process automation. Global technology companies and innovative organizations are rapidly integrating agentic AI architectures and MCP-based integrations into their enterprise workflows to gain a competitive edge. This training provides participants with an end-to-end perspective, ranging from the fundamentals of the agentic AI concept and MCP architecture to agent orchestration and real-world integration scenarios. For your organization, this training enables your technical teams to design and deploy AI agents that are secure, scalable, and aligned with your business goals—boosting operational efficiency, shortening development timelines, and helping you translate your AI investments into tangible business value.
Training Content and Sample Curriculum
In this training, participants embark on a comprehensive journey starting from the theoretical foundations of the agentic AI concept to practical application development with Model Context Protocol (MCP). The sample curriculum covers the following topics:
- Introduction to Agentic AI: The differences between traditional LLM-based assistants and autonomous decision-making agents, and the core components of agent architectures (planning, memory, tool use).
- Model Context Protocol (MCP) Architecture: The purpose behind MCP, its client-server structure, and the data and tool access layers standardized by the protocol.
- Developing MCP Servers: Building your own MCP server and establishing secure connections with file systems, databases, and external APIs.
- Agent Orchestration and Multi-Agent Systems: Coordinated operation of multiple agents, task distribution, error handling, and feedback loops.
- Tool Use and Function Calling: Designing how agents securely trigger external tools, APIs, and enterprise systems.
- Security, Authorization, and Traceability: Auditing agent behavior, defining authorization boundaries, and logging and observability practices.
- Real-World Application Scenarios: End-to-end project work covering autonomous coding assistants, customer support agents, and business process automation.
The curriculum for all our training programs is customized to suit your organization; please contact us for your specific needs. The information provided here is prepared as a sample based on our previous training engagements.
Target Audience
- Software developers and backend engineers
- AI and machine learning engineers
- Product managers and technical project leads
- System architects and DevOps/MLOps specialists
- Digital transformation and innovation teams
- Corporate IT department managers
Organizational Benefits
- Reduction of operational workloads through autonomous AI agents
- Shortened integration times across different systems thanks to the MCP standard
- Internal development teams becoming capable of independently designing agentic AI solutions
- Establishment of enterprise-level security and manageability standards for AI projects
- Gaining a competitive edge by adopting next-generation AI architectures ahead of competitors
- Building the technical competency needed to turn AI investments into tangible ROI
Agentic AI and Model Context Protocol are among the critical technologies that will define the next step of your enterprise AI strategy. Join this training now with Academy Club's expert instructors to prepare your team for this transformation and make a difference in your AI projects.
Curriculum
- The difference between an LLM application and an agent
- Tool/function calling: planning, decision-making, and action generation
- Context engineering
- Error handling and retry mechanisms
- Human-in-the-loop design
Frequently Asked Questions
What topics are covered in the Agentic AI & MCP training?
In this training, we cover comprehensive content ranging from the theoretical foundations of agentic AI to practical application development with the Model Context Protocol (MCP). We address topics such as an introduction to agentic AI, MCP architecture, building MCP servers, agent orchestration and multi-agent systems, tool use and function calling, as well as security and observability. We also guide participants through end-to-end project work based on real-world scenarios such as autonomous coding assistants, customer support agents, and business process automation.
Who is the Agentic AI & MCP training suitable for?
This training is suitable for software developers and backend engineers, as well as AI and machine learning engineers. It is also valuable for product managers and technical project leaders, system architects and DevOps/MLOps specialists, digital transformation and innovation teams, and corporate IT department managers.
What benefits does the Agentic AI & MCP training provide to organizations?
This training enables organizations to reduce operational workloads through autonomous AI agents and shorten integration times between different systems using the MCP standard. Internal development teams become able to design agentic AI solutions independently, and enterprise-grade security and governance standards are established for AI projects. Organizations also gain a competitive advantage by adopting next-generation AI architectures ahead of their rivals and acquire the technical expertise needed to turn AI investments into tangible ROI.
What is the Model Context Protocol (MCP), and how is it covered in the training?
MCP is a standard that enables AI agents to connect securely and consistently to file systems, databases, APIs, and business applications. In this training, we cover the purpose behind MCP's emergence, its client-server architecture, and the data and tool access layers standardized by the protocol. Participants practice building their own MCP servers and establishing secure connections to file systems, databases, and external APIs.
Why is Agentic AI and MCP important for organizations?
Artificial intelligence is no longer just a passive tool that answers questions; it is evolving into agent systems capable of planning, making decisions, and executing tasks end-to-end. Standards like MCP allow these agents to connect securely to enterprise systems, paving the way for revolutionary use cases in areas such as autonomous software development, automated customer service, data analysis, and business process automation. Leading technology companies and innovative organizations are rapidly integrating these architectures into their workflows to gain a competitive edge.