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
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
Shareable certificate
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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.