Hyderabad, India
mailto:training@eduzek.com
Call us: +91-7207950202
Build practical skills in Agentic AI and learn how to design AI agents that can reason through tasks, use tools, retrieve information, and coordinate multi-step workflows. Eduzek’s Agentic AI training covers agent development fundamentals, LangChain, LangGraph, retrieval-augmented generation (RAG), multi-agent systems, CrewAI and Model Context Protocol (MCP). Work through guided projects to understand how agentic applications are designed, developed, evaluated and deployed. Whether you are an aspiring AI engineer, software developer or working professional, explore a structured learning path to build real-world AI agent solutions.
Agentic AI refers to AI systems that can work toward a goal by planning steps, using tools, accessing relevant information and adapting their actions based on results. Unlike a basic prompt-and-response application, an AI agent can participate in a multi-step workflow with appropriate instructions, context and safeguards.
As businesses explore AI-powered automation, agentic systems are being developed for research, customer support, document processing, software development, knowledge retrieval and operational workflows. Building these applications requires more than prompt engineering: learners need to understand model integration, tool calling, state management, retrieval, orchestration, evaluation and responsible deployment.
Eduzek’s Agentic AI course introduces these concepts through a structured learning path and practical projects. You will explore frameworks such as LangChain and LangGraph, learn how retrieval-augmented generation (RAG) can ground responses in relevant information, and understand how tools and multiple agents can work together to solve complex tasks.
The focus is on developing transferable skills to design, build, test and improve AI agent applications—not simply using AI tools.
Learn to build AI agents that connect language models with APIs, external tools, databases and application workflows.
Extend your existing AI skills with agent orchestration, tool calling, retrieval-augmented generation and multi-agent workflows.
Explore AI-powered research, data analysis assistants and intelligent workflows that retrieve information and use tools.
Understand how agentic systems can support repetitive tasks, knowledge work, process automation and business operations.
Build a foundation in AI agent concepts and develop practical projects to demonstrate your skills.
Learn how to evaluate agentic AI use cases and prototype intelligent applications for real business problems.
Build an agent that breaks a research question into tasks, searches approved information sources, summarizes findings and produces a structured report with source references.
Create a document question-answering application using document ingestion, embeddings, vector retrieval and an LLM to produce context-grounded responses.
Develop an AI agent that answers questions from a knowledge base, identifies relevant support tools and routes cases that require human assistance.
Design a workflow in which specialized agents research a topic, draft content and review the output under a defined orchestration process.
Build a stateful workflow with conditional routing, tool calls, checkpoints and controlled recovery from errors.
Prototype an assistant that accesses compatible tools or resources through an MCP server, with appropriate permissions and validation.
Combine query analysis, retrieval, tool use and response evaluation into a knowledge assistant designed around a defined business use case.
| Course Name | Agentic AI Course & Training |
|---|---|
| Learning Focus | AI agents, tool calling, agent orchestration, RAG and multi-agent systems |
| Frameworks and Technologies | LangChain, LangGraph, CrewAI, MCP and agentic RAG |
| Practical Learning | Guided exercises, hands-on projects and a capstone project |
| Course Format | Confirm the available online, live or classroom format with Eduzek |
| Course Duration | Confirm the current course schedule with Eduzek |
| Course Fees | Contact Eduzek for current fees and available payment options |
| Certification | Confirm the applicable certificate and eligibility requirements with Eduzek |
| Prerequisites | Basic programming knowledge and familiarity with Python are recommended |
Progress from Agentic AI fundamentals to tools, retrieval, orchestration and advanced agent workflows through a clearly organized syllabus.
Apply concepts by designing AI assistants, document retrieval systems, task automation workflows and multi-agent applications.
Explore LangChain, LangGraph, CrewAI and MCP concepts to understand different approaches to building and connecting AI agents.
Study how agentic AI can support research, knowledge management, customer support and business process automation.
Learn why testing, evaluation, permissions, human oversight and error handling matter when building AI systems that use tools.
Develop project experience and technical understanding relevant to AI engineering, LLM application development and intelligent automation.
Agentic AI is creating new ways to build intelligent applications, automate multi-step tasks and connect large language models with business tools and knowledge sources. Learning to design and evaluate these systems can help you prepare for roles involving AI application development, LLM integration and intelligent workflow automation.
Design and develop AI agents that plan tasks, call tools, retrieve information and execute controlled workflows. Work may include orchestration, state management, testing and integration with external services.
Build AI-powered applications by integrating language models, retrieval pipelines, APIs and software components. Agentic AI skills can complement broader machine learning and application engineering knowledge.
Develop applications powered by large language models, including assistants, document question-answering systems, conversational interfaces and tool-enabled workflows.
Create generative AI solutions that combine language models with prompts, structured outputs, retrieval, tools and application logic to address practical business needs.
Develop retrieval-augmented generation systems that connect language models to relevant documents and knowledge sources. Responsibilities may include data preparation, embeddings, retrieval quality and response evaluation.
Build AI-enabled workflows that connect business applications, APIs and data sources. Design processes that reduce repetitive manual work while preserving appropriate validation and human oversight.
Explore architectures in which specialized agents coordinate, delegate tasks and exchange information to complete a larger workflow. This work requires careful orchestration, monitoring and evaluation.
Help teams identify suitable AI use cases, prototype solutions and integrate AI agents into existing processes. Strong problem-solving, communication and software development skills are valuable.
Practical projects can help you demonstrate your understanding of agent design, tool integration, RAG, workflow orchestration and evaluation. A portfolio with clear documentation and working demonstrations can support your job applications and technical interviews.
Important: Completing an Agentic AI course does not guarantee a job, salary or promotion. Career outcomes depend on your existing experience, programming ability, project quality, interview performance and employer requirements. These roles may require additional software engineering, cloud, data engineering or machine learning skills.
“I would like to thank Ganesh for his excellent training in PowerApps and Power Automate. Shout out to his patience and knowledge. Ganesh is extremely dedicated, knowledgeable, and professional. He delivered more than what he promised in the demo session and cleared all my doubts. Worth the money!”
“Eduzek is a great platform to learn Power BI, PowerApps, and Power Automate for working professionals. The course tutorial and duration are designed in a perfect way. The teaching staff is very supportive and flexible in terms of class timing.”
“Eduzek helped me in learning Power Automate. I just completed my training and you can reach Ganesh anytime in case of queries and get them resolved, which I liked the best.”
“Excellent training from Ganesh on PowerApps, Power Automate, and SharePoint. I highly recommend Eduzek institute to get trained on these topics.”
“Good training and clearing the doubts with live project examples...”
An Agentic AI course teaches you how to design AI systems that can plan tasks, use tools, retrieve information and execute multi-step workflows. Topics commonly include language models, agent architectures, tool calling, RAG, orchestration, evaluation and responsible AI.
Software developers, AI and machine learning professionals, data practitioners, students and working professionals interested in AI application development can explore Agentic AI. Basic programming knowledge and familiarity with Python are recommended for hands-on development.
A comprehensive syllabus can cover agent fundamentals, LLM integration, prompt engineering, LangChain, RAG, LangGraph, multi-agent systems, CrewAI, MCP, testing, evaluation and a capstone project. Refer to Eduzek's confirmed curriculum for the exact modules included.
Agentic AI course fees depend on the curriculum, duration, training format, mentoring and project support. Contact Eduzek for the current fee, payment options and any applicable offers.
Certification availability and eligibility depend on the specific training program. Contact Eduzek to confirm whether a certificate is included, the completion requirements and the issuing organization.
Basic Python programming is recommended if you want to build AI agents, connect APIs and work with frameworks. Familiarity with HTTP APIs, JSON and basic software development concepts can also help.
Generative AI focuses on creating outputs such as text, images or code. Agentic AI emphasizes systems that use models to pursue goals through actions such as planning, tool use, information retrieval and multi-step workflows. Agentic systems can use generative AI models as part of their implementation.
LangChain provides components and integrations for building LLM applications and agents. LangGraph focuses on orchestrating stateful, often multi-step workflows using graph structures, with capabilities for controlled execution, persistence and human oversight.
A RAG course teaches how to retrieve relevant information from external knowledge sources and provide it to a language model when generating responses. RAG can help AI agents work with organization-specific documents and current knowledge, although retrieval quality and answer accuracy still require evaluation.
Model Context Protocol is a standard for connecting AI applications to compatible external tools and context sources. Learning MCP can help developers understand how to expose and access capabilities through MCP clients and servers, with appropriate access controls.
A project-based course can help you learn the concepts and implementation patterns used to build AI agents. Your ability to develop production-ready applications will depend on practice, software engineering skills, testing, security and the complexity of the use case.
Relevant career paths may include AI engineer, Agentic AI engineer, LLM application developer, Generative AI developer, RAG engineer and AI automation engineer. Employers may also expect experience in programming, APIs, cloud infrastructure, data systems and software development.
Please contact Eduzek to confirm the current online or classroom delivery options, batch schedule and course availability.
Submit an enquiry through Eduzek's course page to ask about the current syllabus, prerequisites, fees, delivery format and upcoming batches. Review the course details before confirming enrolment.