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Applied Agentic AI Pipelines with LangChain

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Applied Agentic AI Pipelines with LangChain

Instructor: Edureka

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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
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.

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Recently updated!

February 2026

Assessments

14 assignments¹

AI Graded see disclaimer
Taught in English

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
  • 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
  • Knowledge Check: Advanced Workflow Engineering and Reliability Techniques•15 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
  • 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
  • Knowledge Check: Intelligent Tooling, ReAct Reasoning, and Multi-Step Retrieval•15 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
  • 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
  • Knowledge Check: Memory Architectures, Vector Routing, and Knowledge Pipelines•15 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
203 Courses•185,285 learners

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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.