This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows.
You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents.
As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration.
By the end of this specialization, you will be able to:
Explain how intelligent agents are built using LangChain and LangGraph
Apply tools, memory, and reasoning to design multi-step agent workflows
Design stateful and multi-agent systems to solve complex use cases
Evaluate and improve agent behavior using observability and feedback techniques
This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design.
Join us now and begin your journey to become an Agentic AI expert.
Projet d'apprentissage appliqué
Across the specialization, learners design and implement agent-based AI systems that solve authentic problems in developer productivity, research, support automation, and incident response.
The projects focus on applying agentic reasoning, tool orchestration, memory design, and workflow control to build intelligent systems that can plan, act, validate results, and adapt across multi-step tasks.
Through hands-on and design-focused projects, learners apply concepts such as structured prompting, multi-tool integration, state management, multi-agent coordination, and observability to create dependable AI solutions for real-world application scenarios. Emphasis is placed on correctness, reliability, and responsible system design, enabling learners to translate agentic AI techniques into practical, deployable solutions.
