Agentic AI Course Overview:
Agentic AI Course is designed to train technical freshers and software developers to build AI-powered, autonomous agents using Python and modern AI frameworks. The program focuses on enabling learners to design, develop, and deploy AI agents within real-world software systems.
Students will gain hands-on experience in integrating Large Language Models (LLMs), designing agent workflows, and embedding AI agents into web applications to meet current and future industry demands.
Master Autonomous AI Agents
Build next-gen intelligent systems with Python—explore the course more and discover career opportunities in Agentic AI.
Training Duration
The training program consists of 140 hours, scheduled for three days each week for three hours daily.
The course is typically completed in approximately four months.
The training is conducted in a hybrid format, allowing students to join either online or in person. For working professionals, weekend classes are available.
Course Fee
₹ 30,500
Limited time offer
Course Structure
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book_2 Module 1: Coding with Python
- • Python syntax and programming fundamentals.
- • Variables, data types, and control structures
- • Functions and modular programming
- • Data structures: lists, tuples, dictionaries, sets
- • File handling and exception management
- • Writing clean and maintainable Python code
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book_2 Module 2: Python for Backend & API Development
- • Object-Oriented Programming in Python
- • Working with JSON and configuration files
- • Consuming and exposing APIs using Python
- • Virtual environments and package management
- • Introduction to asynchronous programming concepts
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book_2 Module 3: AI & Large Language Model Fundamentals
- • Introduction to Artificial Intelligence and Generative AI
- • Understanding Large Language Models (LLMs)
- • Tokens, context windows, and cost considerations
- • Interacting with LLMs using Python
- • Handling and validating AI-generated outputs
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book_2 Module 4: Prompt Engineering for Developers
- • Principles of effective prompt design
- • System, user, and assistant prompts
- • Dynamic prompt creation using Python
- • Structured outputs (JSON-based responses)
- • Prompt optimization and error handling
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book_2 Module 5: Agentic AI Concepts and Architecture
- • Introduction to Agentic AI systems
- • Difference between chatbots and AI agents
- • Agent lifecycle: planning, execution, observation, decision-making
- • Tools, memory, and reasoning mechanisms
- • Single-agent and multi-agent architectures
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book_2 Module 6: Building AI Agents Using Python
- • Designing task-oriented AI agents
- • Tool calling and function execution
- • Integrating APIs and databases with agents
- • Managing agent context and memory
- • Developing custom agent logic
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book_2 Module 7: RAG (Retrieval Augmented Generation)
- • Introduction to RAG architecture
- • Vector databases and embeddings (Pinecone, Chroma, FAISS)
- • Building knowledge bases for agents
- • Document retrieval and context injection
- • Combining RAG with agentic workflows
- • Optimizing retrieval quality and relevance
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book_2 Module 8: Agent Frameworks (Python-Based)
- • Overview of Python agent frameworks
- • Building agents using LangChain
- • Multi-agent systems using CrewAI
- • Framework selection and architectural considerations
- • Best practices for scalable agent development
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book_2 Module 9: AI Agents with Backend Frameworks
- • Developing AI agent services using FastAPI / Django
- • Designing REST APIs for AI-powered applications
- • Authentication and user-specific agent behavior
- • Background processing and task orchestration
- • Managing long-running AI workflows
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book_2 Module 10: Frontend Integration & Application Embedding
- • Exposing AI agents through APIs
- • Integrating agents into web applications
- • Handling asynchronous responses
- • Webhooks and agent triggers
- • UI considerations for AI-driven features
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book_2 Module 11: Cost Optimization & Efficiency
- • Token optimization strategies
- • Caching mechanisms for AI responses
- • Model selection based on task complexity
- • Streaming responses for better user experience
- • Batch processing and request optimization
- • Cost monitoring and budget management
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book_2 Module 12: Production Deployment & DevOps
- • Containerization with Docker for AI applications
- • Cloud deployment strategies (AWS, Azure, GCP)
- • Scaling AI agent systems
- • CI/CD pipelines for AI applications
- • Environment management and configuration
- • Production monitoring and alerting
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book_2 Module 13: Real-World Integration Patterns
- • Email automation agents
- • Slack/Teams bot integration
- • Database query and analysis agents
- • Web scraping and data extraction agents
- • Scheduling and cron-based automation
Project (Industry-Focused)
Students will develop end-to-end AI agent applications, or agents within existing applications to automate particular workflows..
Certification as Trainee AI-ML Engineer:
On successful completion of the course, students get certified as Trainee AI-Ml Engineer, jointly by Ejobindia and the development firm Sysalgo Technologies.
