Agentic RAG & Knowledge Engineering enables AI agents to autonomously retrieve, reason, and update knowledge using structured data, vector databases, and intelligent workflows for real-world AI systems.
Level
Advanced
Duration
8 Weeks
Placement Client
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About Agentic RAG & Knowledge Engineering
Agentic RAG & Knowledge Engineering by TechPratham is a cutting-edge program designed to build intelligent AI systems that can autonomously retrieve, reason, and act on knowledge. The course focuses on advanced Retrieval-Augmented Generation (RAG), agent-based workflows, vector databases, and knowledge graph design. Learners gain hands-on experience in creating scalable, explainable, and production-ready AI agents capable of handling complex real-world tasks. With a strong emphasis on practical implementation, this program helps professionals master autonomous decision-making, multi-agent coordination, and enterprise-grade knowledge engineering using modern AI frameworks and tools.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks & Updated Content
Access to updated presentation decks shared during live training sessions.
e-Book
E-book provided by TechPratham. All rights reserved.
Assignments & MCQs
Module-wise assignments and MCQs provided for practice.
Video Recording
Daily Session would be recorded and shared to the candidate.
Projects
Live projects will be provided for hands-on practice.
Resume Building
Expert-guided resume building with industry-focused content support.
Interview Preparation
Comprehensive interview preparation with real-time scenario practice.
Agentic RAG & Knowledge Engineering Course Curriculum
Foundations of Agentic AI & RAG
Understand how autonomous AI agents and Retrieval-Augmented Generation work together to solve complex real-world problems.
Knowledge Engineering Fundamentals
Learn how to structure, organize, and manage knowledge using ontologies, taxonomies, and schemas for intelligent systems.
Retrieval-Augmented Generation (RAG) Architecture
Explore modern RAG architectures, chunking strategies, embeddings, and retrieval optimization techniques.
Vector Databases & Semantic Search
Master vector databases and semantic search to enable fast, accurate, and scalable AI information retrieval.
Agentic RAG Workflows
Design autonomous agent workflows with tool-calling, memory, and multi-step reasoning capabilities.
Knowledge Graphs & Graph-Based RAG
Enhance RAG systems using knowledge graphs, entity relationships, and graph-powered contextual retrieval.
Agentic Ai Courses
No related courses found
Additional Program Highlights
Upcoming Batches
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Who Should Take Agentic RAG & Knowledge Engineering
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Working Professionals
Ops/Administrators/HR
Developers
BA/QA Engineers
Cloud / Infra
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Job Roles For Agentic RAG & Knowledge Engineering
AI Automation Architect
RAG Implementation Consultant
Enterprise AI Engineer
AI Orchestration Architect
LLM Solutions Architect
Multi-Agent Systems Engineer
Knowledge Engineering Specialist
Agentic RAG Engineer
AI Automation Architect
RAG Implementation Consultant
Enterprise AI Engineer
Key Projects
Agentic RAG & Knowledge Engineering
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Workday – Workday Smart Knowledge RAG
Scenario: Agentic RAG system unifies HR, finance and policy data, enabling contextual answers, governed retrieval, and continuous learning across enterprise knowledge secured.
Live Work:
- Built RAG over HCM and Finance data
- Designed agent based query routing
- Applied role based data governance
Microsoft – Microsoft Graph RAG Agent
Scenario: Agentic RAG platform connects emails, files and chats, building trusted knowledge graphs that deliver precise answers, audit trails, and adaptive reasoning teams.
Live Work:
- Integrated Outlook and SharePoint data
- Created multi agent reasoning flow
- Implemented traceable answer sources
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Salesforce – Salesforce Insight RAG
Scenario: Agentic RAG engine merges CRM data, cases and documents, providing real time insights, explainable answers, and smarter actions across sales service flows.
Live Work:
- Connected cases, leads and KB data
- Built explainable answer generation
- Enabled agent driven next best action
UiPath – UiPath Cognitive RAG Bot
Scenario: Agentic RAG solution links bots with enterprise knowledge, enabling automated decisions, accurate responses, and self improving workflows across operations at scale.
Live Work:
- Embedded RAG inside automation bots
- Enabled decision making from knowledge
- Implemented feedback learning loop
Placement Process
Live Class with Industry Use Cases
- Interactive learning
- Real-world examples
- Expert guidance
- Doubt resolution
Daily Assignment & Weekly Assessments
- Hands-on practice
- Concept reinforcement
- Weekly evaluation
- Timely feedback
Project Testing & Deployment
- Project development
- Testing practice
- Deployment exposure
- Portfolio creation
Interview Preparation (Mock, PD, Alumni Session)
- Mock interviews
- Personal development
- Alumni guidance
- Confidence building
Resume Building
- Resume crafting
- Industry-focused format
- Skills highlighting
- Professional presentation
Placement
- Job assistance
- Interview support
- Career guidance
- Opportunity access
Knowledge Center
Recently Placed Candidates
We’re Hiring
News Highlights
Our Success Mantra
Commitment
- Ensuring quality training every day
Fulfillment
- Meeting learning goals with confidence
Accomplishment
- Students achieving industry-ready expertise
Our Learner Voice
Beyond Courses:
Additional Support We Provide
24/7 Support
LinkedIn Profile
Resume Writing
Alumni Sessions
Interview Preparation
Live Projects
What Makes TechPratham Training Different?
What is Agentic RAG & Knowledge Engineering and why is it important in enterprise AI?
How is Agentic RAG different from traditional RAG implementations?
What skills will I gain from the Agentic RAG & Knowledge Engineering course?
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Industry-Recognized Certification
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