Agentic AI

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Training Key Features

  • Job-Oriented Skills
  • 30-Day Structured Roadmap
  • End-to-End ML Workflow
  • Beginner-Friendly Approach
Learning Format: Live Training Download Syllabus

What will be Cover ?

Introduction to AI Agents
  • Introduction to AI Agents
  • Evolution from LLMs to Agents
  • Real-world use cases of agentic systems
  • AI Agent components (LLM, tools, memory, planner, executor)
Agentic Workflows
  • Introduction to workflows
  • Workflow components
  • Event-driven vs step-based workflows
  • AI workflows vs Agentic workflows
Building Agentic Systems
  • Building common AI workflows using n8n
  • Tool integration with LLMs
  • Decision branching and control flow
  • Building advanced agentic workflows using n8n
Context Engineering for AI Agents
  • Understanding context in AI systems
  • Prompt engineering for agents
  • Context structuring and optimization
  • Applying latest context engineering techniques
Augmented AI Agents
  • Overview of augmented AI agents
  • Tool-augmented agents
  • Retrieval-augmented generation (RAG)
  • Building Agentic RAG systems
Building Multi-Agent Systems
  • Multi-agent system fundamentals
  • Agent roles and coordination
  • Communication and orchestration patterns
  • Designing real-world multi-agent architectures
Debugging & Evaluating AI Agents
  • Monitoring agentic systems
  • Debugging agent behaviors and failures
  • Evaluation techniques (LLM-as-a-Judge, heuristic metrics)
  • Building evaluation pipelines for agents
Optimizing AI Agents
  • Industry best practices for agent optimization
  • Adding memory and guardrails to agents
  • Model selection frameworks
  • Latency and cost optimization techniques
Deploying Agentic Applications
  • Production considerations (latency, cost, security, privacy)
  • Deployment strategies for agentic systems
  • Frontend integration and user experience
  • Building and deploying a real-world agentic application
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Course Detail

Benifits 

This course is designed to help you build strong foundations in Python, Math, and Statistics while gaining hands-on experience with real-world Machine Learning and Deep Learning projects. By the end of the program, you’ll be confident in using industry-standard tools like NumPy, Pandas, Scikit-learn, and TensorFlow to build, evaluate, and deploy ML models. With multiple projects and a capstone added to your GitHub portfolio, you’ll be job-ready and equipped with practical skills for a career in Data Science and AI.