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

kurs ist nicht verfügbar in Deutsch (Deutschland)

Wir übersetzen es in weitere Sprachen.

Applied Agentic AI Pipelines with LangChain

Dieser Kurs ist Teil von Spezialisierung „Agentic AI Engineering“

Dozent: Edureka

Bei enthalten

Mehr erfahren

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Data Processing
Kategorie: Embeddings
Kategorie: Memory Management
Kategorie: Data Transformation
Kategorie: Large Language Modeling
Kategorie: Generative AI Agents
Kategorie: Context Management
Kategorie: Automation
Kategorie: Workflow Management
Kategorie: Artificial Intelligence
Kategorie: LLM Application

Werkzeuge, die Sie lernen werden

Kategorie: Generative AI
Kategorie: JSON

Wichtige Details

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Februar 2026

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Unterrichtet in Englisch

91%

of learners achieved a positive career outcome

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Agentic AI Engineering“

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In diesem Kurs gibt es 4 Module

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.

Das ist alles enthalten

12 Videos5 Lektüren4 Aufgaben

12 VideosInsgesamt 68 Minuten

Specialization Introduction6 Minuten
Course Introduction5 Minuten
Designing Runnable Sequences, Branching Logic, and Parallel Execution5 Minuten
Demonstration: Creating a RunnableSequence for Data Enrichment6 Minuten
Demonstration: Implementing Conditional Routing with RunnableBranch7 Minuten
OutputFixingParser, Error Handling Techniques, and Automated Retries5 Minuten
Demonstration: Auto-Correcting Invalid JSON Outputs - I5 Minuten
Demonstration: Auto-Correcting Invalid JSON Outputs - II4 Minuten
Demonstration: Applying Retry Logic for Pipeline Reliability7 Minuten
TransformChain Workflows, Data Normalization, and Post-Processing Strategies5 Minuten
Demonstration: Building a Text Normalization Pipeline5 Minuten
Demonstration: Creating a Scoring and Ranking Processor7 Minuten

5 LektürenInsgesamt 70 Minuten

Course Syllabus15 Minuten
Advanced Runnable Workflow Patterns for Reliable AI Pipelines15 Minuten
Ensuring Accuracy and Consistency in Model Outputs15 Minuten
Data Post-Processing Strategies for Scalable AI Systems15 Minuten
Module Summary: Advanced Workflow Engineering and Reliability Techniques10 Minuten

4 AufgabenInsgesamt 33 Minuten

Practice Assignment: Mastering LangChain Runnables6 Minuten
Practice Assignment: Output Correction and Pipeline Stabilization6 Minuten
Practice Assignment: Data Transformation and Post-Processing Techniques6 Minuten
Knowledge Check: Advanced Workflow Engineering and Reliability Techniques15 Minuten

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.

Das ist alles enthalten

14 Videos4 Lektüren4 Aufgaben

14 VideosInsgesamt 86 Minuten

Multi-Tool Routing Strategies, Fallback Handling, and Prioritization6 Minuten
Demonstration: Building an Intelligent Tool Router - I6 Minuten
Demonstration: Building an Intelligent Tool Router - II7 Minuten
Thought–Action–Observation Loop and ReAct Enhancements5 Minuten
Demonstration: Implementing a Multi-Hop ReAct Workflow - I7 Minuten
Demonstration: Implementing a Multi-Hop ReAct Workflow - II7 Minuten
Demonstration: Adding Verification to ReAct Tool-Chaining - I7 Minuten
Demonstration: Adding Verification to ReAct Tool-Chaining - II6 Minuten
Demonstration: Adding Verification to ReAct Tool-Chaining - III4 Minuten
Multi-Query Retrieval, Fusion Techniques, and Multi-Hop Reasoning5 Minuten
Demonstration: Generating Multi-Query Expansions with Fusion - I8 Minuten
Demonstration: Generating Multi-Query Expansions with Fusion - II5 Minuten
Demonstration: Building a Multi-Hop RAG Reasoning Chain - I7 Minuten
Demonstration: Building a Multi-Hop RAG Reasoning Chain - II7 Minuten

4 LektürenInsgesamt 55 Minuten

Building Stateful and Context-Aware Tools15 Minuten
ReAct Agents: Extensibility with Middleware and LangGraph15 Minuten
Retrieval-Augmented Generation (RAG) Architecture15 Minuten
Module Summary: Intelligent Tooling, ReAct Reasoning, and Multi-Step Retrieval10 Minuten

4 AufgabenInsgesamt 33 Minuten

Practice Assignment: Intelligent Tool Routing and Orchestration6 Minuten
Practice Assignment: Advanced ReAct Reasoning Patterns6 Minuten
Practice Assignment: Multi-Query Retrieval and Multi-Hop RAG Workflows6 Minuten
Knowledge Check: Intelligent Tooling, ReAct Reasoning, and Multi-Step Retrieval15 Minuten

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.

Das ist alles enthalten

9 Videos4 Lektüren4 Aufgaben

9 VideosInsgesamt 50 Minuten

Vector Memory Architecture and Adaptive Routing Techniques6 Minuten
Demonstration: Building a Vector Memory Store with Adaptive Selection - I5 Minuten
Demonstration: Building a Vector Memory Store with Adaptive Selection - II5 Minuten
Hybrid Memory Systems with Layered Context6 Minuten
Demonstration: Combining Memory Types for Composite Retrieval - I6 Minuten
Demonstration: Combining Memory Types for Composite Retrieval - II6 Minuten
Knowledge Tools and Metadata Ranking6 Minuten
Demonstration: Building a Self-Correcting Knowledge Query Pipeline - I5 Minuten
Demonstration: Building a Self-Correcting Knowledge Query Pipeline - II5 Minuten

4 LektürenInsgesamt 55 Minuten

Vector Memory Patterns for Stateful and Adaptive AI Systems15 Minuten
Composite Memory Design for Persistent Deep Agents15 Minuten
Integrating External Knowledge with LangChain Agents Using Retrievers15 Minuten
Module Summary: Memory Architectures, Vector Routing, and Knowledge Pipelines10 Minuten

4 AufgabenInsgesamt 33 Minuten

Practice Assignment: Vector Memory and Adaptive Retrieval6 Minuten
Practice Assignment: Composite Memory: Vector, Summary, and Entity Models6 Minuten
Practice Assignment: Knowledge Tools and Retrieval Optimization6 Minuten
Knowledge Check: Memory Architectures, Vector Routing, and Knowledge Pipelines15 Minuten

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.

Das ist alles enthalten

1 Video1 Lektüre2 Aufgaben1 Diskussionsthema

1 VideoInsgesamt 2 Minuten

Course Summary2 Minuten

1 LektüreInsgesamt 30 Minuten

Practice Project: Building a Research Assistant Agent for Strategy Teams30 Minuten

2 AufgabenInsgesamt 60 Minuten

End Course Knowledge Check: Advanced Workflow Engineering and Reliability Techniques30 Minuten
Building Scalable and Reliable Agent Pipelines with LangChain30 Minuten

1 DiskussionsthemaInsgesamt 5 Minuten

Describe Your Learning Journey5 Minuten

Erwerben Sie ein Karrierezertifikat.

Fügen Sie dieses Zeugnis Ihrem LinkedIn-Profil, Lebenslauf oder CV hinzu. Teilen Sie sie in Social Media und in Ihrer Leistungsbeurteilung.

Dozent

Edureka

185 Kurse175.899 Lernende

von

Edureka

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Häufig gestellte Fragen

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.

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¹ Einige Aufgaben in diesem Kurs werden mit AI bewertet. Für diese Aufgaben werden Ihre Daten in Übereinstimmung mit Datenschutzhinweis von Courseraverwendet.