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.

Applied Agentic AI Pipelines with LangChain

Applied Agentic AI Pipelines with LangChain
This course is part of Agentic AI Engineering Specialization

Instructor: Edureka
Access provided by Interbank
Recommended experience
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.
Skills you'll gain
- Artificial Intelligence
- Data Preprocessing
- Metadata Management
- Automation
- Responsible AI
- Generative AI
- Workflow Management
- Context Management
- Data Transformation
- JSON
- Embeddings
- Large Language Modeling
- Skills section collapsed. Showing 11 of 12 skills.
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February 2026
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There are 4 modules in this course
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
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
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
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
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