This course introduces the principles and practice of AI Agent Orchestration and Scaling, blending conceptual understanding with hands-on system design. You’ll learn how to coordinate, monitor, and optimize multiple AI agents that work together to deliver intelligent, autonomous workflows — with a special focus on building scalable customer support solutions powered by AI.
AI Agent Orchestration and Scaling
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Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
8 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Design orchestration frameworks that coordinate autonomous agents effectively.
Implement scaling strategies to manage high-performance, multi-agent systems.
Monitor and evaluate agent workflows to ensure consistency and reliability.
Develop autonomous AI agents that learn, adapt, and optimize over time.
Skills you'll gain
Details to know
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Taught in English
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There are 4 modules in this course
This module explores multimodal AI and stateful orchestration using LangGraph to build intelligent, context-aware agents. You’ll learn to connect visual, textual, and API inputs for real-time problem diagnosis and decision-making. By the end, you’ll have built a visually informed, multi-tool triage agent capable of handling complex, multimodal workflows autonomously.
What's included
12 videos5 readings4 assignments
This module focuses on enabling long-term memory and dynamic re-planning in autonomous agents. You’ll learn to design knowledge graphs and memory modules that let agents recall past experiences and adapt their actions. By the end, you’ll build a self-correcting, feedback-driven agent capable of real-time learning and continuous improvement through long-term memory integration.
What's included
10 videos4 readings4 assignments
This module brings together orchestration, governance, and large-scale deployment of autonomous agents. You’ll implement guardrails, audit trails, and human-in-the-loop controls for safe operations, then deploy and scale workflows and containerization. By the end, you’ll have an end-to-end, production-ready autonomous system capable of governed, scalable decision-making.
What's included
11 videos4 readings4 assignments
This module provides learners with an opportunity to synthesize their knowledge and demonstrate mastery of AI systems. Learners will review key concepts from memory-augmented agents, real-time data integration, multimodal orchestration, and governance frameworks. They will complete graded, scenario-based assessments to apply their understanding in building and managing collaborative, secure, and scalable agent ecosystems.
What's included
1 video1 reading2 assignments
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Frequently asked questions
The course aims to teach how to design, build, and scale autonomous agents that can reason, plan, and act using LangGraph integrating multimodal inputs, long-term memory, and dynamic orchestration for enterprise environments.
LangGraph extends LangChain by focusing on stateful orchestration — allowing developers to create graph-based agent workflows with persistent state, conditional routing, and memory-aware decision nodes.
Multimodal inputs (text, image, voice, etc.) enable agents to understand real-world contexts more accurately. For example, diagnosing issues from screenshots or combining text and visual data for richer reasoning.
More questions
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.