Agentic AI marks the shift from AI that simply responds to AI that reasons, plans, and acts on its own. Where earlier generative AI tools needed constant human prompting, agentic systems can break down goals, call tools, retrieve knowledge, and complete multi-step workflows with minimal supervision. kodestree’s Agentic AI training is built around this shift, combining live instruction with hands-on labs covering agent architecture, multi-agent orchestration, RAG pipelines, and real-world deployment – so you graduate with skills that map directly to what hiring teams want right now.
Agentic AI Students Also Learn
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Prerequisites
- Basic understanding of Python programming
- Familiarity with fundamental machine learning or generative AI concepts (helpful, not mandatory)
- Working knowledge of APIs and how web services communicate
- No prior experience with LangChain, CrewAI, or agent frameworks is required – these are taught from the ground up
Course Objectives
By the end of this program, you’ll be able to design, build, and deploy intelligent agents that work independently across real business workflows.
- Understand the core architecture behind autonomous AI agents
- Build multi-agent systems using LangGraph and CrewAI
- Implement Agentic RAG and GraphRAG for context-aware reasoning
- Integrate agents with external tools and APIs using MCP (Model Context Protocol)
- Deploy and monitor agents in production environments
- Apply human-in-the-loop and governance practices for safe agent behavior
What You Will Learn
This Agentic AI Online Training takes you from foundational concepts to advanced, deployable agent systems:
- Foundations of agentic AI vs. traditional generative AI workflows
- Prompt engineering and reasoning patterns for autonomous agents
- Building single-agent and multi-agent pipelines
- Memory management, planning, and task decomposition in agents
- Retrieval-Augmented Generation (RAG) and Agentic RAG techniques
- Connecting agents to real-world tools, databases, and workflow automation systems
- Observability, evaluation, and debugging of live agent systems
- Ethical safeguards, compliance, and responsible AI deployment practices
- A capstone project simulating an enterprise-grade autonomous agent use case
Essential Skills You Will Gain
In this training program, you will gain the following skills that are essential to build a strong foundation.
- Agentic AI Architecture
- Autonomous AI Agent Development
- Multi-Agent System Design
- LangGraph Workflows
- CrewAI Orchestration
- Retrieval-Augmented Generation (RAG)
- Agentic RAG & GraphRAG
- Prompt Engineering
- AI Reasoning & Planning
- Tool Calling & API Integration
- Model Context Protocol (MCP)
- Memory Management for AI Agents
- Task Planning & Decomposition
- Vector Database Integration
- DSPy Programming
- AutoGen Framework
- Workflow Automation with n8n
- FastAPI Integration
Who is this course for?
This course is designed for those who want to build, deploy, and manage autonomous AI agents as part of their AI career.
- Software developers and backend engineers
- Machine learning engineers and data scientists
- Generative AI practitioners looking to specialize
- Product managers and architects working on AI-driven products
- Working professionals exploring a career pivot into AI
- Students and freshers aiming for entry-level AI roles
Tools and Technologies Covered
This Agentic AI Certification gives you hands-on exposure to the exact stack used in production agentic systems today.
- LangChain and LangGraph
- CrewAI
- AutoGen
- MCP (Model Context Protocol)
- DSPy
- n8n for workflow automation
- FastAPI for backend integration
- Streamlit for rapid agent interfaces
- Docker for containerized deployment
- Vector databases for RAG pipelines
Projects You Will Work With
AI Research & Report Agent
“Give it a topic, and it plans its own research, gathers sources, and writes you a cited report.”
Build an autonomous research agent that takes a broad query, breaks it into sub-questions, uses web search and retrieval tools to gather information, then synthesizes findings into a structured, cited report- iterating on gaps in its own research before finalizing.
- Skills: Task decomposition, agentic loops (plan → act → observe), web search/retrieval tool use, self-critique and iteration
- Time: 10-12 hours
- Deliverable: CLI or web demo that takes a topic and outputs a cited research report + a log showing the agent’s planning/reasoning steps
Multi-Agent Trip Planning System
“One agent researches destinations, another checks budget and logistics, a third negotiates the final itinerary – all talking to each other.”
Build a multi-agent system with specialized sub-agents (research agent, budget/logistics agent, itinerary-builder agent) coordinated by an orchestrator agent. Agents pass structured messages to each other, handle disagreements (e.g., budget agent rejecting an over-budget plan), and converge on a final output.
- Skills: Multi-agent orchestration, inter-agent communication protocols, role-based prompting, state management across agents
- Time: 10-14 hours
- Deliverable: Working multi-agent demo + architecture diagram showing agent roles and message flow
Autonomous Web Task Agent
“Tell it what you need done on a website, and it browses, clicks, fills forms, and reports back- no API required.”
Build a browser-automation agent that takes a natural-language goal (e.g., “find the cheapest flight from X to Y and summarize options”), plans a sequence of browser actions, executes them via a computer-use/browser tool, handles unexpected page states, and reports results back to the user.
- Skills: Computer/browser-use tool integration, action planning under uncertainty, error recovery, grounding text goals into UI actions
- Time: 10-12 hours
- Deliverable: Working demo video/recording of the agent completing a real browser task + documented action trace
Career Outcomes
Completing this training opens doors to some of the fastest-growing roles in the AI job market.
- Agentic AI Engineer
- AI Agent Developer
- LLM/RAG Engineer
- AI Solutions Architect
- Generative AI Engineer
- AI Automation Specialist
- AI Product Engineer
Average Salary of Agentic AI Professionals
Agentic AI skills currently command a strong pay premium over generalist AI roles, and that gap is expected to widen as adoption scales through 2026.
| Experience Level | Estimated Annual Salary (India) | Estimated Annual Salary (Global) |
| Entry-Level (0-2 yrs) | ₹6 – ₹12 LPA | $90,000 – $120,000 |
| Mid-Level (2-5 yrs) | ₹12 – ₹30 LPA | $120,000 – $180,000 |
| Senior-Level (5+ yrs) | ₹30 – ₹60+ LPA | $180,000 – $250,000+ |
Top Hiring Companies
The following are the top companies that hire Agentic AI Professionals on a smart salary package.
- OpenAI
- Google DeepMind
- Cognizant
- Infosys
- Deloitte
- and Fortune 500 Startups in the AI automation space.
Why Choose kodestree for This Training?
kodestree’s certified trainers have guided thousands of professionals into successful AI careers, backed by practical, industry-aligned training. The following are the reasons learners choose kodestree for this training.
- Live, instructor-led sessions with industry practitioners
- Curriculum updated continuously to match real-world agentic AI tools
- Hands-on projects and a capstone aligned with enterprise use cases
- Lifetime access to recorded sessions and course material
- Dedicated mentor support and doubt-resolution sessions
- Certificate recognized by hiring teams and recruiters
- Flexible weekday and weekend batch options
- Resume building, mock interviews, and placement assistance
