What skills will I gain from this course?

By the end of this course, you'll be able to design intelligent agent pipelines, implement multi-step reasoning workflows, optimize retrieval systems, and build adaptive memory architectures using LangChain.

How long will it take to complete the course?

This course is designed to be completed in 4 weeks, with a recommended study pace of 3 - 4 hours per week. You can progress at your own pace, revisiting videos, readings, and assignments as needed.

Do I need programming knowledge to take this course?

A foundational understanding of Python and AI principles is recommended, but no advanced programming skills are required. All tools and workflows are taught step-by-step using LangChain.

What career opportunities can this course lead to?

Mastering intelligent agent systems and LangChain opens doors to roles in AI-driven software development, automation engineering, and AI operations. You'll be equipped for positions in agent-based application development, AI system optimization, and memory architecture design.

Will I receive a certificate upon completion?

Yes, you will receive a certificate of completion after successfully finishing all modules and assessments. This certificate validates your expertise in building intelligent agent systems and enhances your professional profile.

How is this course different from other AI programs?

Unlike general AI courses, this program focuses on LangChain’s application for building intelligent agent systems. It emphasizes hands-on demos, real-world workflows, and tool-based exercises using LangChain for multi-step reasoning and complex agent development.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Applied Agentic AI Pipelines with LangChain

This course is part of Agentic AI Engineering Specialization

Instructor: Edureka

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Design advanced workflows for intelligent agent systems with LangChain.
Apply multi-step reasoning and ReAct workflows to optimize AI agents.
Construct adaptive memory architectures and integrate multi-query retrieval.
Evaluate and apply error handling and output correction for pipeline reliability.

Details to know

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Build your subject-matter expertise

This course is part of the Agentic AI Engineering Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

This program explores advanced techniques for designing intelligent agent pipelines using LangChain, equipping developers and AI enthusiasts with the skills to build scalable, reliable, and efficient AI systems. You’ll start by mastering LangChain’s core functionalities, including advanced workflow engineering, output correction, and data transformation for agent systems.

Next, you’ll dive into intelligent tooling, learning how to implement multi-step reasoning, ReAct-driven workflows, and complex tool orchestration. You’ll also explore cutting-edge retrieval techniques, multi-query reasoning, and adaptive memory architectures, building systems capable of handling dynamic, real-time data across multiple steps. By the end of this program, you will be able to: -Define the foundational concepts of LangChain and its role in intelligent agent design. -Master LangChain runnables, data transformations, and advanced error handling techniques. -Implement intelligent tool routing and multi-hop reasoning using ReAct workflows. -Build robust multi-query retrieval systems with adaptive memory and composite retrieval strategies. -Optimize knowledge query pipelines with self-correcting features for more accurate insights. -Design scalable, stateful agent systems with persistent memory and vector routing. This program is designed for developers and AI practitioners interested in building powerful agent-driven applications using LangChain. A background in Python, machine learning, and basic AI concepts will enhance your learning experience. Learners require a reliable internet connection, a modern web browser, and access to LangChain documentation and tools. No specialized hardware or software installation is necessary. Join us to explore the cutting-edge of intelligent agent design with LangChain, and gain the expertise needed to build the next generation of AI systems.

Design advanced LangChain workflows using runnable sequences, branching logic, and parallel execution to support complex agent pipelines. Engineer reliable workflows by applying output correction, structured error handling, and automated retry mechanisms. Stabilize LLM-driven systems by addressing common failure patterns and invalid outputs. Apply data transformation and post-processing techniques to normalize, score, and refine results.

What's included

12 videos5 readings4 assignments

12 videos Total 68 minutes

Specialization Introduction 6 minutes
Course Introduction 5 minutes
Designing Runnable Sequences, Branching Logic, and Parallel Execution 5 minutes
Demonstration: Creating a RunnableSequence for Data Enrichment 6 minutes
Demonstration: Implementing Conditional Routing with RunnableBranch 7 minutes
OutputFixingParser, Error Handling Techniques, and Automated Retries 5 minutes
Demonstration: Auto-Correcting Invalid JSON Outputs - I 5 minutes
Demonstration: Auto-Correcting Invalid JSON Outputs - II 4 minutes
Demonstration: Applying Retry Logic for Pipeline Reliability 7 minutes
TransformChain Workflows, Data Normalization, and Post-Processing Strategies 5 minutes
Demonstration: Building a Text Normalization Pipeline 5 minutes
Demonstration: Creating a Scoring and Ranking Processor 7 minutes

5 readings Total 70 minutes

Course Syllabus 15 minutes
Advanced Runnable Workflow Patterns for Reliable AI Pipelines 15 minutes
Ensuring Accuracy and Consistency in Model Outputs 15 minutes
Data Post-Processing Strategies for Scalable AI Systems 15 minutes
Module Summary: Advanced Workflow Engineering and Reliability Techniques 10 minutes

4 assignments Total 33 minutes

Knowledge Check: Advanced Workflow Engineering and Reliability Techniques 15 minutes
Practice Assignment: Mastering LangChain Runnables 6 minutes
Practice Assignment: Output Correction and Pipeline Stabilization 6 minutes
Practice Assignment: Data Transformation and Post-Processing Techniques 6 minutes

Build intelligent agent pipelines that dynamically route tools, manage prioritization, and handle fallback execution. Implement advanced ReAct reasoning patterns using multi-step Thought-Action-Observation loops with verification and tool chaining. Enable deeper reasoning by applying multi-query retrieval, fusion strategies, and multi-hop RAG workflows. Coordinate reasoning, tooling, and retrieval across complex, multi-stage tasks.

What's included

14 videos4 readings4 assignments

14 videos Total 86 minutes

Multi-Tool Routing Strategies, Fallback Handling, and Prioritization 6 minutes
Demonstration: Building an Intelligent Tool Router - I 6 minutes
Demonstration: Building an Intelligent Tool Router - II 7 minutes
Thought–Action–Observation Loop and ReAct Enhancements 5 minutes
Demonstration: Implementing a Multi-Hop ReAct Workflow - I 7 minutes
Demonstration: Implementing a Multi-Hop ReAct Workflow - II 7 minutes
Demonstration: Adding Verification to ReAct Tool-Chaining - I 7 minutes
Demonstration: Adding Verification to ReAct Tool-Chaining - II 6 minutes
Demonstration: Adding Verification to ReAct Tool-Chaining - III 4 minutes
Multi-Query Retrieval, Fusion Techniques, and Multi-Hop Reasoning 5 minutes
Demonstration: Generating Multi-Query Expansions with Fusion - I 8 minutes
Demonstration: Generating Multi-Query Expansions with Fusion - II 5 minutes
Demonstration: Building a Multi-Hop RAG Reasoning Chain - I 7 minutes
Demonstration: Building a Multi-Hop RAG Reasoning Chain - II 7 minutes

4 readings Total 55 minutes

Building Stateful and Context-Aware Tools 15 minutes
ReAct Agents: Extensibility with Middleware and LangGraph 15 minutes
Retrieval-Augmented Generation (RAG) Architecture 15 minutes
Module Summary: Intelligent Tooling, ReAct Reasoning, and Multi-Step Retrieval 10 minutes

4 assignments Total 33 minutes

Knowledge Check: Intelligent Tooling, ReAct Reasoning, and Multi-Step Retrieval 15 minutes
Practice Assignment: Intelligent Tool Routing and Orchestration 6 minutes
Practice Assignment: Advanced ReAct Reasoning Patterns 6 minutes
Practice Assignment: Multi-Query Retrieval and Multi-Hop RAG Workflows 6 minutes

Develop advanced memory systems that enable intelligent agents to retain context and retrieve relevant knowledge over time. Apply vector memory and adaptive routing techniques to improve retrieval accuracy and efficiency. Combine vector, summary, and entity-based memory models to support layered context and long-term reasoning. Optimize knowledge retrieval using metadata-aware tools and self-correcting query pipelines.

What's included

9 videos4 readings4 assignments

9 videos Total 50 minutes

Vector Memory Architecture and Adaptive Routing Techniques 6 minutes
Demonstration: Building a Vector Memory Store with Adaptive Selection - I 5 minutes
Demonstration: Building a Vector Memory Store with Adaptive Selection - II 5 minutes
Hybrid Memory Systems with Layered Context 6 minutes
Demonstration: Combining Memory Types for Composite Retrieval - I 6 minutes
Demonstration: Combining Memory Types for Composite Retrieval - II 6 minutes
Knowledge Tools and Metadata Ranking 6 minutes
Demonstration: Building a Self-Correcting Knowledge Query Pipeline - I 5 minutes
Demonstration: Building a Self-Correcting Knowledge Query Pipeline - II 5 minutes

4 readings Total 55 minutes

Vector Memory Patterns for Stateful and Adaptive AI Systems 15 minutes
Composite Memory Design for Persistent Deep Agents 15 minutes
Integrating External Knowledge with LangChain Agents Using Retrievers 15 minutes
Module Summary: Memory Architectures, Vector Routing, and Knowledge Pipelines 10 minutes

4 assignments Total 33 minutes

Knowledge Check: Memory Architectures, Vector Routing, and Knowledge Pipelines 15 minutes
Practice Assignment: Vector Memory and Adaptive Retrieval 6 minutes
Practice Assignment: Composite Memory: Vector, Summary, and Entity Models 6 minutes
Practice Assignment: Knowledge Tools and Retrieval Optimization 6 minutes

Review and consolidate the key concepts covered throughout the course, including advanced workflows, intelligent tooling, reasoning patterns, retrieval strategies, and memory architectures. Apply these skills in a hands-on practice project by building a multi-tool research agent that integrates end-to-end agent pipeline design. Demonstrate mastery through a final graded assignment focused on designing reliable and intelligent agent pipelines.

What's included

1 video1 reading2 assignments1 discussion prompt

1 video Total 2 minutes

Course Summary 2 minutes

1 reading Total 30 minutes

Practice Project: Building a Research Assistant Agent for Strategy Teams 30 minutes

2 assignments Total 60 minutes

End Course Knowledge Check: Advanced Workflow Engineering and Reliability Techniques 30 minutes
Building Scalable and Reliable Agent Pipelines with LangChain 30 minutes

1 discussion prompt Total 5 minutes

Describe Your Learning Journey 5 minutes

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Instructor

Edureka

138 Courses 134,837 learners

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Edureka

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Frequently asked questions

This course is ideal for developers, AI practitioners, and data scientists looking to design intelligent agent systems, automate workflows, and optimize AI reasoning using LangChain. No prior coding experience is required, but a background in Python and AI concepts will be beneficial.

The course covers LangChain architecture, multi-step reasoning, ReAct workflows, error handling, memory architectures, multi-query retrieval, and knowledge optimization. You’ll also gain hands-on experience in building adaptive, scalable agent systems with advanced capabilities.

Yes! The course includes interactive demos and practice assignments using LangChain to build intelligent agent systems. You’ll apply skills to real-world workflows, implement multi-step reasoning, and integrate adaptive memory and knowledge retrieval systems.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.