Start typing to search courses...

Type in the search box to find courses
CoursesHigh Demanding
Advanced Generative Ai and Agentic Ai Training in London
High Demanding

Advanced Generative Ai and Agentic Ai Training in London

Master Advanced Generative & Agentic AI skills with Techpratham’s hands-on training in London—learn cutting-edge AI techniques and applications.

5/5(4,890 Reviews)

Level

Advanced

Duration

8 Weeks

About
Training Plan
Course Curriculum
New Batch
Projects
Certificate
Testimonials
FAQ
Interview FAQ

Placement Client

Accenture Logo
AWS Logo
Capgemini Logo
Deloitte Logo
Genpact Logo
HP Logo
Intel Logo
Microsoft Logo
Infosys Logo
Zoho Logo
Zelis Logo
Wipro Logo
Saint Gobain Logo
ONX Logo
Nava Logo
Infosys Logo
HCL Logo
Egon Zehnder Logo
Cognizant Logo
Bosch Logo
Bank of America Logo
Accenture Logo
AWS Logo
Capgemini Logo
Deloitte Logo
Genpact Logo
HP Logo
Intel Logo
Microsoft Logo
Infosys Logo
Zoho Logo
Zelis Logo
Wipro Logo
Saint Gobain Logo
ONX Logo
Nava Logo
Infosys Logo
HCL Logo
Egon Zehnder Logo
Cognizant Logo
Bosch Logo
Bank of America Logo

About Advanced Generative Ai and Agentic Ai Training in London

Elevate your career with Techpratham’s Advanced Generative AI and Agentic AI Training in London, a premier program designed for Machine Learning Engineers, Software Developers, Data Scientists, Graduates, Tech Enthusiasts, Business Leaders, Product Managers, and Entrepreneurs looking to lead the autonomous intelligence revolution. Our live interactive classes provide hands-on experience in building complex multi-agent systems and sophisticated generative workflows, ensuring you stay at the forefront of 2026’s tech landscape. Most importantly, we bridge the gap to your dream role with our 100% assured placement program, offering direct access to London’s top-tier hiring partners and personalized career mentorship.

Video Thumbnail

Training Plan

01
About trainer

About trainer

Working professional who is carrying more then 10 years of industry experience.

02
Decks & Updated Content

Decks & Updated Content

Access to updated presentation decks shared during live training sessions.

03
e-Book

e-Book

E-book provided by TechPratham. All rights reserved.

04
Assignments & MCQs

Assignments & MCQs

Module-wise assignments and MCQs provided for practice.

05
Video Recording

Video Recording

Daily Session would be recorded and shared to the candidate.

06
Projects

Projects

Live projects will be provided for hands-on practice.

07
Resume Building

Resume Building

Expert-guided resume building with industry-focused content support.

08
Interview Preparation

Interview Preparation

Comprehensive interview preparation with real-time scenario practice.

Advanced Generative Ai and Agentic Ai Course Curriculum

Python for AI & Automation (Foundation Layer)

Python for AI & Automation (Foundation Layer) builds core Python skills to automate tasks and create a strong base for AI and machine learning development.

Python Essentials for AI
Python syntax, data types, control flow
Functions, modules, virtual environments
OOP concepts (important for agents)
Python for Automation & AI
File handling, APIs, JSON
Web requests, REST API consumption
Async programming basics
Logging, exception handling
AI-Ready Python Libraries
NumPy, Pandas
Requests, FastAPI (intro)
LangChain utilities (preview)

Introduction to Agentic AI & LLM Ecosystem 

A foundational overview of how autonomous AI agents work with large language models to plan, reason, and execute tasks across modern AI systems.

What is Agentic AI?
Role of LangGraph, AutoGen, CrewAI in the ecosystem
OpenAI vs Azure OpenAI vs AWS Bedrock
Introduction to foundational concepts: Agents, Tasks, Graphs

Basics of LangChain and LangGraph

An introductory overview of using LangChain and LangGraph to build, connect, and orchestrate intelligent, multi-step AI workflows.

LangChain recap: Chains, Tools, Memory
LangGraph architecture and why it matters
Installation, environment setup, and first LangGraph DAG

Exploring LangGraph Core Concepts 

A concise introduction to building stateful, multi-step AI workflows using LangGraph for agent orchestration and decision-making.

Nodes, Edges, State Machines
Understanding transitions and handlers
Building a simple agentic task flow

Python SDK and Node Configuration

 An overview of setting up the Python SDK and configuring nodes to build, connect, and manage scalable AI workflows efficiently.

Deep dive into LangGraph Python SDK
Defining nodes and reactive transitions
Testing individual components with unit test strategy

Multi-Agent Setup with LangGraph

A practical introduction to designing and orchestrating multiple AI agents that collaborate and share state using LangGraph.

Multi-agent interaction via graph state
Introducing dynamic task allocation
Conditional logic and loops in graphs

High Demanding Courses

No related courses found

Additional Program Highlights

Learning Materials

Comprehensive study materials and resources

HD
Resume Writing

Professional resume building session

HD
Interview Preparation

Master your interview skills

HD
Live Project Demo

Real-world project demonstrations

HD

Upcoming Batches

Can't find a batch you were looking for?

Who Should Enroll in this Advance Generative Ai and Agentic Ai Training

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Job Roles After Completing Advanced Generative Ai and Agentic Ai Training

Agentic AI Engineer

Multi-Agent Systems (MAS) Architect

Generative AI Solutions Architect

Key Projects

Advanced Generative Ai and Agentic Ai

Accenture

AccentureAgentic M&A Post-Merger Integration Suite


Scenario: A London private equity firm needs to accelerate post-deal value capture by embedding autonomous AI agents directly into complex M&A workflows.

Live Work:

  • Deployed multi-agent systems to map data.
  • Automated target entity system migration playbooks.
  • Standardized cross-border compliance checks.
Outcome: Reduced legacy infrastructure transition times from 12 months down to 90 days.
Tata Consultancy Services

Tata Consultancy ServicesAutonomous IT Run-Cost Optimization Engine


Scenario: A major London financial institution requires intent-driven autonomous agents to manage hybrid cloud estates and eliminate technology debt.

Live Work:

  • Orchestrated multi-agent incident swarming tools.
  • Automated 50 million lines of code migration.
  • Built continuous reliability guardrails.
Outcome: Achieved a 35% total reduction in IT operational run-costs within 12 months.
Deloitte

DeloitteAgentic Supply Chain Resiliency Network


Scenario: A global retail enterprise headquartered in London wants an autonomous system to sense and remediate supply chain disruptions in real-time.

Live Work:

  • Integrated vision analytics for quality checks.
  • Built autonomous negotiation routing agents.
  • Implemented live supplier telemetry tracking.
Outcome: Discovered and rerouted freight delays automatically without human intervention.
KPMG

KPMGSovereign AI Financial Compliance Auditor


Scenario: A multinational bank in London's Canary Wharf requires a secure, localized generative AI framework to handle cross-border tax audit checks.

Live Work:

  • Embedded data-readiness governance fabrics.
  • Structured policy-driven vector databases.
  • Maintained human-in-the-loop validation tools.
Outcome: Scaled risk assessment workflows across 12 distinct regulatory jurisdictions.
Mobile Banner

Latest HiringNEW

No hiring posts

Recently Placed Candidates

No placements available

Latest HiringNEW

No hiring posts

Our Success Mantra

Commitment Icon
Commitment

  • Ensuring quality training every day

Commitment Icon
Fulfillment

  • Meeting learning goals with confidence

Commitment Icon
Accomplishment

  • Students achieving industry-ready expertise

Our Learner Voice

carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel
carousel

Beyond Courses:

Additional Support We Provide

24/7 Support

LinkedIn Profile

Resume Writing

Alumni Sessions

Interview Preparation

Live Projects

What is the difference between Generative AI and Agentic AI?

What are the key prerequisites for this advanced training?

Does this course cover "Agentic Workflows" like ReAct or Reflection?

What tools and frameworks will be used during the training?

Will we learn how to build Multi-Agent Systems (MAS)?

What is "Agentic RAG" and how does it differ from standard RAG?

You’ve trained a multimodal generative model that hallucinate plausible but incorrect facts when given ambiguous prompts; how do you diagnose and mitigate this behavior in production?

Explain how you would design an agentic AI system to autonomously plan tasks across dynamic environments with partial observability?

How do you evaluate the reproducibility and robustness of generative AI outputs across different data distributions?

 Describe how you’d implement differential privacy in training a large language model for sensitive enterprise data.

In generative pipelines, how can you reconcile model creativity with strict safety & compliance without sacrificing usability?

What practical steps are needed to deploy an agentic AI in financial decision‑making while minimizing risk & regulatory exposure?

About Advanced Generative Ai and Agentic Ai Certification

This advanced certification focuses on the transition from static prompt engineering to dynamic, goal-oriented automation. It covers the deployment of autonomous systems that plan, use external tools, and self-correct to execute multi-step workflows.

Course Benefits & Duration


  • Trending Market Demand: Focuses on high-demand enterprise skills including Retrieval-Augmented Generation (RAG), Multi-Agent Orchestration, LLMOps, vector databases (ChromaDB, FAISS), and Model Context Protocol (MCP).
  • Core Frameworks: Hands-on training using LangChain, LangGraph, CrewAI, and AutoGen to build scalable, production-ready AI pipelines.
  • Duration: Typically 5 to 7 months (~150+ hours), combining live instructor-led sessions, industry-aligned labs, and a practical Capstone project.


Exam Pattern & Timing


  • Format: Proctored online exam consisting of Multiple-Choice Questions (MCQs) paired with a Project-Based Practical Evaluation (coding lab/case study).
  • Timing: 100 to 120 minutes for the theoretical segment; project submissions have flexible portfolio deadlines.
  • Passing Score: Requires a minimum score of 70% to clear.


Conclusion

This program bridges the gap between foundational Large Language Models (LLMs) and production-grade Agentic-first business solutions, making it essential for AI engineers and data scientists looking to lead automation initiatives.

Industry-Recognized Certification

Certificate
Advanced Generative Ai and Agentic Ai

News Highlights

TechPratham Introduces Hire-Train-Deploy Model to Transform HR & ERP Talent in the AI Era
TechPratham Empowering Future Professionals Through AI-Focused HR & ERP Training

Featured In

Featured Logo 1Featured Logo 2Featured Logo 3Featured Logo 4Featured Logo 5Featured Logo 6Featured Logo 7Featured Logo 8Featured Logo 9Featured Logo 10Featured Logo 11Featured Logo 12
TechPratham Gains Recognition for Bridging the HR & ERP Skills Gap with Hire-Train-Deploy
TechPratham's Hire-Train-Deploy Approach Reshaping HR & ERP Careers in the AI-Driven Industry
India Flag

India

Head Office

G-31, 1st Floor, Sector-3, Noida - 201301

India Flag+91-8882178896
WhatsApp
USA Flag+1 (343) 477-0926
WhatsApp
India Flag

India

Noida Office

C-2, Sector-1, Noida, Uttar Pradesh - 201301

India Flag+91-8882178896
WhatsApp
USA Flag+1 (343) 477-0926
WhatsApp
India Flag

India

Hyderabad Office

LVS Arcade, 6th Floor, Hitech City, Hyderabad

India Flag+91-8383058741
WhatsApp
USA Flag+1 (343) 477-0926
WhatsApp
© 2026 TechPratham. All rights reserved.An ISO 9001:2015 Certified Company