Agentic AI Course in Bangalore
Build production-ready AI agents using LangGraph, MCP, Agentic RAG, tracing, evaluation and deployment through live sessions, reviewed projects and direct instructor support.
Live Agentic AI Training in Bangalore
Attend structured live classes with real-time explanations, technical discussions and practical implementation guidance. Recordings are provided for revision and missed sessions.
The course is structured for professionals balancing full-time roles. Live online delivery removes the need for regular travel while preserving instructor interaction and cohort-based learning.
Get support with framework selection, architecture decisions, debugging, retrieval design, evaluation and project implementation throughout the program.
Move beyond simple chatbot examples and learn how agent workflows are structured, evaluated, traced, improved and prepared for deployment.
What You Will Learn and Build
Agent Workflow Design
Use LangChain, LangGraph, CrewAI and AutoGen to compare orchestration patterns, manage state, route tools and design multi-step agent workflows.
MCP and Tool Integration
Work with MCP concepts, structured tool contracts and Playwright-based browser workflows to connect agents with external systems more reliably.
Agentic RAG, Tracing and Evaluation
Build retrieval workflows with reranking, grounding and citations, then use traces and evaluation sets to identify failures and improve output quality.
Deployment and Runtime Fundamentals
Learn how agent systems move beyond notebooks through local and AWS-based deployment patterns, runtime monitoring and failure-handling fundamentals.
View the Complete Agentic AI Curriculum
Review the complete module breakdown, project structure, prerequisites and certification details on the flagship Agentic AI course page.
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What you'll find on the main page
Complete Module Breakdown
See the full module-by-module structure of the 12-week program in one place.
Hands-On Project Structure
Review the project list, capstone direction and evaluation approach covered during the program.
Certificate Requirements
Understand the sessions, assignments and project reviews required to earn the course completion certificate.
Program Format and Support
Review the live online format, instructor support, recordings and career support included with the program.
Why Bangalore Professionals Choose Live Agentic AI Training
Bangalore's software, product, GCC and AI teams increasingly need professionals who can build structured agent workflows, retrieval systems and reliable AI features.
The live online format gives working professionals access to instructor-led sessions, recordings, technical discussions and project feedback without requiring regular travel to a classroom. It is designed for professionals who want structured learning alongside a full-time role.
Why Professionals Choose This Agentic AI Course in Bangalore
Compare the program based on teaching format, technical depth, projects and learner support.
| Features | School of Core AI | Other Institutes |
|---|---|---|
| Learning Format | ✓Live mentor-led sessions with recordings, discussions and implementation support. | ✗May rely mainly on recorded lessons or limited instructor interaction. |
| Technical Coverage | ✓LangGraph, MCP, Agentic RAG, evaluation, tracing and deployment taught as one connected workflow. | ✗Often focused on prompt patterns, basic demos or isolated tool walkthroughs. |
| Project Depth | ✓Projects emphasise tool use, grounded retrieval, debugging and explainable architecture choices. | ✗Projects may remain at the demo level with limited review or feedback. |
| Evaluation and Debugging | ✓Quality is treated as measurable through evaluation sets, trace reviews and regression thinking. | ✗Evaluation is often missing, which makes it difficult to move beyond prototypes. |
| Working-Professional Support | ✓Designed for professionals who need structured teaching and technical access without offline attendance. | ✗Schedules and support may not be built around full-time working professionals. |
| Career Preparation | ✓You leave with systems and trade-offs you can explain clearly in interviews and project discussions. | ✗Learners may finish with examples that are difficult to present in technical interviews. |
Live mentor-led sessions with recordings, discussions and implementation support.
May rely mainly on recorded lessons or limited instructor interaction.
LangGraph, MCP, Agentic RAG, evaluation, tracing and deployment taught as one connected workflow.
Often focused on prompt patterns, basic demos or isolated tool walkthroughs.
Projects emphasise tool use, grounded retrieval, debugging and explainable architecture choices.
Projects may remain at the demo level with limited review or feedback.
Quality is treated as measurable through evaluation sets, trace reviews and regression thinking.
Evaluation is often missing, which makes it difficult to move beyond prototypes.
Designed for professionals who need structured teaching and technical access without offline attendance.
Schedules and support may not be built around full-time working professionals.
You leave with systems and trade-offs you can explain clearly in interviews and project discussions.
Learners may finish with examples that are difficult to present in technical interviews.
Compare Before You Enroll
Check whether you need agentic depth or a neighboring path
These comparisons help you decide whether your next step is agent systems, broader GenAI foundations, or application-building first.
AI Developer Course vs Agentic AI Course
Decide whether you need application-building foundations or a dedicated agent systems track first.
Open comparisonGenerative AI Course vs Agentic AI Course
Choose between broad GenAI foundations and agent-focused orchestration depth.
Open comparisonRAG vs Agentic RAG
Choose between standard retrieval pipelines and more agentic, tool-using retrieval workflows.
Open comparisonCrewAI vs AutoGen vs LangGraph
Compare three agent frameworks by speed, collaboration style, and orchestration control.
Open comparisonWho This Course Is For
Software Engineers
Developers who want to build agent systems with structured orchestration, tool use and measurable reliability. The course helps you move from application development to agentic workflow design.
AI and Machine Learning Professionals
Practitioners who already understand ML basics and want practical depth in orchestration, retrieval, evaluation and deployment of agent systems.
Data Professionals
Data analysts, engineers and scientists who want to add Agentic RAG, tracing and agent workflow skills to their existing toolkit.
DevOps and Platform Engineers
Engineers responsible for deployment, observability and runtime who want to apply those skills to agent systems, including deployment patterns and operational reliability.
Technical Product Professionals
Product managers and technical leaders who need to understand agent architecture, failure modes and implementation trade-offs to make better product decisions.
Technical Career Switchers
Professionals from engineering-heavy backgrounds who want to move toward applied AI, agent engineering or AI product roles with a project-backed portfolio.
Agentic AI Projects You Will Build
Build systems that demonstrate workflow design, retrieval, tool use, evaluation and deployment.
Projects include a tool-using agent workflow, an Agentic RAG system with grounded retrieval, an evaluation and tracing setup, and a deployment-focused capstone. The emphasis is on implementation choices, failure analysis and clear technical documentation.
Tools and Frameworks Covered
Agent Orchestration
LangChain, LangGraph, CrewAI and AutoGen for state management, branching, tool routing and multi-agent workflow patterns.
MCP and Tool Integration
MCP concepts, structured tool interfaces, LangFlow and Playwright for connecting agents with external tools and browser workflows.
Retrieval, Tracing and Evaluation
Agentic RAG, vector retrieval, reranking, grounding, LangSmith, Langtrace and evaluation workflows for measuring and improving quality.
Deployment Fundamentals
Local and AWS-based deployment patterns, runtime considerations, debugging, monitoring and release-oriented implementation.
12-Week Agentic AI Curriculum
Foundation
- Core concepts
- Tools & setup
- Hands-on intro
Build
- Advanced techniques
- Guided projects
- Industry tools
Specialise
- Elective tracks
- Capstone project
- Peer reviews
Launch
- Portfolio prep
- Mock interviews
- Placement drive
Course Completion Certificate
Learners who complete the required sessions, assignments and project reviews receive a course completion certificate covering agent workflows, Agentic RAG, tool integration, evaluation, tracing and deployment fundamentals.
Certificate of Completion
Issued by School of Core AI upon successful completion of the programme
Learn With an Agentic AI Peer Community
Learn Alongside Engineers and AI Practitioners
Where Our Learners Work
What Learners Say About the Program
Agentic AI Skills Across Industry
Agentic AI Career Opportunities in Bangalore
Agentic AI skills are relevant across applied AI, software engineering, retrieval systems, AI platforms, automation and technical product roles.
Employers increasingly value professionals who can explain orchestration, retrieval, evaluation, tracing, tool integration and deployment, not only prompting.
Build multi-step agent workflows with tools, orchestration logic, trace review and reliability discipline inside real products.
Own retrieval-heavy systems with grounding, reranking, citations, evaluation loops and clear reasoning about output quality.
Support runtime behaviour, deployment patterns, observability, cost awareness and release-oriented thinking for agent systems.
Define agent features, guardrails, evaluation plans, rollout expectations and collaboration patterns with engineering teams.
Use tools, browser workflows, retrieval and workflow automation to build useful internal systems that are easier to explain and maintain.
Skills Employers Look for in Agentic AI Engineers
Strong Agentic AI engineers combine workflow design, retrieval, evaluation, tool integration, observability and deployment fundamentals.
Compare Related AI Learning Paths
Choose a program based on whether your priority is agent engineering, broader Generative AI development or production deployment.
Course Fee, Schedule and Next Bangalore Cohort
The ₹35,000 course fee covers the 12-week live online program, session recordings, guided projects, project reviews, course completion certificate and learner support. Applicable taxes are additional.
Final fee, EMI plans and any ongoing offers will be confirmed by your counsellor based on your batch, mode and payment preference.
Frequently Asked Questions
The program is delivered live online with mentor-led sessions, recordings, project reviews and instructor support. It is designed for learners and working professionals in Bangalore, but it is not a permanent offline classroom course.