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Advanced Certification in Agentic AI Systems and Design

2,817 Ratings

  • Master the future of AI: Build Autonomous Agents, Multi-Agent Systems, and Production-Grade RAG Pipelines using CrewAI, AutoGen, LangGraph, and Modern Python.
  • Live online Interactive sessions from Top Industry Experts and AI Architects.
  • Build 10+ AI Agents capable of Reasoning, Planning, and Tool Execution.
  • 100% Placement support for AI Engineering roles.
In Collaboration With
Microsoft

Key Highlights

5 Months Live Interactive Sessions
200+ Hrs Self-paced Videos and Labs
15+ Industry-relevant Agentic Projects
Live Classes from AI Architects and IIT Faculty
Career Services by Intellipaat
Master Tools: CrewAI, AutoGen, LangGraph, DSPy
24/7 Technical Support
Google-Reviews 3109
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About the Agentic AI Systems & Design Course Overview

Why Pursue a Career in Agentic AI?

The Era of “Prompt Engineering” is ending. The Era of “Agent Engineering” has begun. Companies are no longer looking for people who can just “chat” with ChatGPT. They need engineers who can build systems that work autonomously.

  • High Demand: Enterprises are racing to deploy autonomous agents to automate complex workflows.
  • Lucrative Salaries: AI Agent Architects and AI Engineers command top-tier salaries, often exceeding traditional Data Science roles.
  • Impact: Move from analyzing data to building systems that take action and solve business problems in real-time.
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Agentic AI Systems and Design Career Transitions

55% Average Salary Hike

45 LPA The Highest Salary

12000+ Career Transitions

500+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Meet the Agentic AI Systems and Design Training Mentors

What role do Agentic AI professionals play?

AI Agent Architect

Designs the foundational cognitive frameworks and blueprints that empower AI systems to reason, plan, and execute complex objectives autonomously.

Agentic Workflow Engineer

Constructs and integrates the operational pipelines that connect autonomous agents with external APIs and tools to execute multi-step processes.

Machine Learning Engineer (LLM Focus)

Optimizes and fine-tunes the core language models that drive the reasoning and decision-making engines of agentic systems.

AI Product Developer

Transforms advanced agentic capabilities into intuitive, scalable, and user-facing applications designed to solve real-world business challenges.

Automation Architect

Strategizes the enterprise-wide deployment and orchestration of autonomous systems to scale operations and maximize systemic efficiency.

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25+ Skills Covered under this Agentic AI Systems Course

Agentic AI Systems

Modern Python 3.11+

Multi-Agent Orchestration

Retrieval-Augmented Generation (RAG)

CrewAI

AutoGen

LangGraph

DSPy

Function Calling

Advanced Prompt Engineering

Chain-of-Thought Reasoning

Asynchronous Programming

Pydantic and Data Validation

LLM Observability

Tool Integration

Model Context Protocol (MCP)

Memory Architectures

Vector Embeddings

System Design Patterns

Autonomous Workflows

Context-Augmented Generation (CAG)

Dependency Management

Low-Code Automation (n8n)

Ethical AI Guardrails

Graph-Based Workflows

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Tools You Will Master

SQL Langchain langgraph crewai autogen docker Pinecone n8n 1 Make 1

Course Fees

Online Classroom Preferred

Weekend (Sat-Sun)

28 Feb 2026 08:00 PM - 11:00 PM
Weekday (Tue-Fri)

03 Mar 2026 07:00 AM - 09:00 AM
Weekend (Sat-Sun)

07 Mar 2026 08:00 PM - 11:00 PM
Weekend (Sat-Sun)

14 Mar 2026 08:00 PM - 11:00 PM
60,021 10% OFF Expires in

EMI Starts at

₹5,000

We partnered with financing companies to provide very competitive finance options at 0% interest rate

Financing Partners

EMI Partner

The credit facility is provided by a third-party financing company and any arrangement with such financing companies is outside Intellipaat’s purview.

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

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Agentic AI Systems and Design Course Syllabus

Live Course

Module 1 - Modern Python (3.11+) for Agentic Systems

This module builds a strong Python foundation required for modern AI and agentic system development.

Key Topics

  • Python fundamentals and best practices using modern IDEs (VS Code / PyCharm); clean code, modular design, debugging, and logging
  • Modern Python (3.11+) features: type hints (list[str], | None), dataclasses, TypedDict, Protocol, generics, pattern matching (match-case)
  • Dependency and environment management: Poetry for project structuring and reproducible builds; uv for ultra-fast package installation and dependency resolution
  • Object-Oriented Programming (OOP): classes, inheritance, polymorphism, abstraction; composition-first design and common design patterns (Factory, Strategy, Adapter)
  • Async/Await and concurrency patterns: asyncio, coroutines, task groups, parallel API calls, retries, and timeouts; when to use async vs threads/processes
  • Python for Data Science and AI pipelines: role of Python across ingestion, preprocessing, feature engineering, modeling, and evaluation
  • Working with Python APIs and SDKs; intro to vibe coding and productivity using GitHub Copilot
  • Data manipulation and numerical computing: NumPy for vectorized operations; Pandas for joins, groupby, transformations, and analytics workflows. Introduction to Polars
  • Data preprocessing techniques: handling missing values, normalization, encoding, scaling, and outlier treatment
  • Exploratory data analysis and visualization using Plotly and Dash for interactive analytics applications
  • Pydantic or Pandera for data validation, schema enforcement, and type safety in AI pipelines, APIs, and agent/tool inputs
  • DSPy: declarative programming for LLM workflows, structured reasoning, and prompt optimization
  • Langfuse: observability, tracing, evaluation, and monitoring of LLM and agent behavior
  • Design Pattern in Python

This module introduces the core concepts behind agentic AI systems and how they differ from traditional AI applications.

Key Topics

  • What are AI Agents?
  • Definitions, characteristics, and evolution of LLM-based apps
  • Agentic system architecture:
  • Planner, Executor, Memory, Tools, and Feedback loops
  • Agent lifecycles and decision-making flows
  • Design principles for scalable and reliable agent systems
  • Monolithic vs modular agent architectures

A deep dive into the core building blocks that power intelligent, autonomous agents.

Key Topics

  • Advanced Prompt Engineering
  • System prompts, role prompts, and instruction hierarchies
  • Self-reflection, self-correction, and chain-of-thought optimization
  • Prompt Strategies: Few-shot, zero-shot, self-ask, reflection, and planner–executor prompts
  • Memory in Agentic Systems
  • Types of Memory: Short-term, long-term, episodic, semantic, and hybrid memory architectures
  • Memory storage using vector databases and structured stores
  • Retrieval-Augmented Generation (RAG)
  • Grounding agents in proprietary and enterprise data
  • Chunking strategies, embeddings, and retrieval pipelines
  • Tool Integration (Function Calling)
  • Connecting agents to APIs, databases, and enterprise systems
  • Tool selection, execution, and error handling
  • RAG, TAG, and CAG
  • RAG: Retrieval-based grounding
  • TAG (Tool-Augmented Generation): Tool-driven reasoning
  • CAG (Context-Augmented Generation): Context injection and orchestration
  • Function Calling and Tool Use
  • Deterministic execution
  • Error recovery and validation
  • MCP (Model Context Protocol)
  • Standardizing context exchange between agents, tools, and models

This module focuses on building, testing, and deploying practical single-agent systems.

Key Topics

  • Setting up the agent development environment
  • Designing agent workflows:
  • Planning, reasoning, tool usage, and memory
  • Building a single-agent system with:
  • Planning logic
  • Tool integration
  • RAG pipelines
  • Testing and evaluation:
  • Debugging agent behavior
  • Iterative improvement using feedback loops
  • Real-world use cases:
  • Finance automation (analysis, reporting, reconciliation)
  • HR workflows (resume screening, interview assistance)
  • Content creation and research automation

This module explores how multiple agents collaborate to solve complex problems and how such systems are deployed in production.

Key Topics

  • Designing multi-agent workflows:
  • Task decomposition
  • Role-based agents
  • Sequential and parallel execution
  • Framework deep dive:
  • CrewAI: Agent roles, tasks, and coordination
  • AutoGen: Conversational agents and agent-to-agent communication
  • Deployment strategies:
  • Cloud and on-prem deployment considerations
  • Scaling and performance optimization
  • Observability and monitoring:
  • Logs, traces, and metrics
  • Governance and ethics:
  • Responsible AI
  • Data privacy and compliance
  • Guardrails
  • Safety constraints, policy enforcement, and hallucination control
  • LangGraph
  • Graph-based agent workflows
  • State management and branching logic
  • No-Code / Low-Code Agent Builders
  • n8n and Make.com for orchestration
  • ChatGPT Agents for rapid prototyping

A hands-on, end-to-end project focused on designing a production-grade agentic system.

Key Topics

  • Problem definition and requirement analysis
  • Tool identification and integration strategy
  • Designing memory architecture
  • Prototyping a multi-agent system using:
  • LangChain
  • CrewAI
  • AutoGen
  • Evaluation, iteration, and optimization
  • Strategic pitch:
  • Business use case
  • ROI analysis
  • Ethical considerations
  • Deployment roadmap

This module bridges industry practice with cutting-edge academic research.

Key Topics

  • Implementing research ideas into working prototypes
  • Translating research insights into production-ready designs
  • Understanding future trends in agentic AI
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Agentic AI Systems and Design Projects

Career Services

Career Services
guaranteed
Placement Assistance
job portal
Exclusive access to Intellipaat Job portal
Mock Interview Preparation
One-on-One Career Mentoring Sessions
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Career Oriented Sessions
linkedin 1
Resume & LinkedIn Profile Building
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Agentic AI Systems and Design Certification

MasterAgentic AI Skills and Earn Your Industry Certificate

  • Get certification from Microsoft for SQL
  • Learn from IIT Faculty and Top Industry Professionals
  • Get the placement assistance and visibility with our 3100+ Hiring Partners

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Agentic AI Systems and Design FAQs

How is this

A Gen AI course typically covers how to use LLMs to produce text or images (like ChatGPT). This Agentic AI course is a bit more advanced. It teaches you how to create Autonomous Agents that can plan, reason, use tools (like web search, coding, or APIs), and perform complex tasks without any human interaction. This course is geared towards developers who want to create production-grade systems, not just chatbots.

This program is best suited for Data Scientists, Python Developers, AI/ML Engineers, and Technical Architects looking for a skill upgrade. If you want to move beyond simple “prompt engineering” and build intelligent software that can solve problems autonomously, this course is for you.

Yes, the basic knowledge of the Python programming language is necessary, as this is a technical course, and hands-on programming is necessary. Although we will be discussing Modern Python (3.11+) in Module 1, your prior programming experience with the Python language will give you a competitive advantage.

Yes, Intellipaat & Microsoft certificate is globally recognized . This will help you demonstrate your expertise in state-of-the-art technologies in AI and make you a specialist in Agentic Systems.

The program is offered in virtual live classes learning format. Your instructors are Industry experts, SME’s and IIT faculty.

To obtain the most out of this course, it is required that the candidates commit themselves to around 8-10 hours every week, which includes live sessions, reading, and hands-on labs and projects.

You will get 24/7 technical support and a learning portal. Moreover, you also get a facility for 1:1 sessions with Teaching Assistants (TAs) for doubt clearance and a forum with your peers to discuss your problems and share your insights.

You will be proficient in the industry standard for creating AI agents, which includes Crew AI and Auto Gen for multi-agent orchestration, Lang Graph for stateful workflows, DSPy for prompt optimization, and Vector Databases like Pinecone and Weaviate for memory. Lang Fuse for observability and n8n for low-code automation are also part of the technology stack.

The curriculum has been designed into 7 comprehensive modules. It begins with Modern Python and Agent Architecture, then Memory and RAG (Retrieval-Augmented Generation), Tool Integration (Function Calling), and then a deep dive into Multi-Agent Collaboration. It ends with a Capstone Project and deployment strategies.

You will have over 15+ industry-specific projects to work on. For instance, you might work on developing an Autonomous Equity Research Analyst, a Multi-Agent HR Recruitment System, an Intelligent Customer Support Bot, or an Automated Content Creation Studio. These projects mimic a variety of enterprise environments.

The Capstone is a hands-on, end-to-end project in which you design and develop a production-grade Agentic System from scratch. In the capstone project, you get to design a business problem, choose the correct agent architecture (Single vs. Multi-Agent), and deploy the solution. This is a portfolio piece to present to potential employers.

Yes, unlike most other theory courses, we will emphasize production a lot. You will learn how to deploy agents on Cloud environments, how to manage dependencies with Poetry/Docker, and how to set up observability for monitoring agent performances and costs.

Some of the emerging and high-demand job roles you can aim for after completing the course are AI Agent Architect, AI Systems Engineer, Agentic Workflow Developer, Machine Learning Engineer (LLM Focus), and AI Product Developer.

Yes, the industry is moving rapidly from the domain of “Chatbots” to the domain of “Agents.” Finance, Healthcare, Tech, and Customer Service organizations are hiring engineers to develop autonomous systems to cut down on costs. Currently, there is a greater demand for Agentic AI skills than the availability of experts in this domain.

Although a salary increase depends upon various factors like your present designation and performance, experts in specific AI Agent skills tend to attract higher salaries. Even alumni of Intellipaat’s advanced AI programs have reported average salary increments of 55%. They have successfully transitioned to high-end product companies.

At Intellipaat, you get access to exclusive career services such as resume building, LinkedIn profile development, and mock interview sessions with experts. In addition to this, you get access to our exclusive job portal and referrals to our network of 500+ hiring partners.

It is possible, but it is quite challenging without any background in coding. If you are not from a technical background, we suggest that you take some of the programming courses we offer before attempting this advanced certification. If you are good at logical questions and are willing to go the extra mile to learn the programming language, we are always there to help you through the transition.

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