How does Long-Term Memory (LTM) improve agent intelligence?

LTM allows agents to store and recall prior experiences, user interactions, or problem contexts, enabling personalized responses, adaptive learning, and better decision-making over time.

What is the role of Knowledge Graphs in this architecture?

Knowledge Graphs model relationships between users, tools, and error patterns — serving as a semantic backbone for reasoning and context retrieval within the agent’s memory system.

How do governance and guardrails protect the system in production?

Governance mechanisms, like irreversible action guardrails and audit trails, ensure agents act safely, comply with policies, and route high-risk actions through human-in-the-loop checks.

Can these LangGraph agents be deployed at scale?

Yes. The course covers deployment via Flask APIs, containerization (Docker/Kubernetes), and scaling strategies for distributed or edge-based agent orchestration.

What kind of real-world applications can learners build after this course?

Learners can design enterprise-grade support or diagnostic agents that analyze multimodal data, plan tasks dynamically, perform self-correction, and integrate with real-time APIs for autonomous decision-making.

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.

AI Agent Orchestration and Scaling

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AI Agent Orchestration and Scaling

Dieser Kurs ist Teil von Spezialisierung „Autonomous AI Agent Systems and Orchestration“

Dozent: Edureka

Bei enthalten

Mehr erfahren

4 Module

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

Stufe Fortgeschritten

Empfohlene Erfahrung

8 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

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

Stufe Fortgeschritten

Empfohlene Erfahrung

8 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Product Support
Kategorie: Artificial Intelligence
Kategorie: Agentic systems
Kategorie: Generative AI Agents
Kategorie: Memory Management
Kategorie: AI Integrations

Werkzeuge, die Sie lernen werden

Kategorie: Model Deployment
Kategorie: AI Orchestration
Kategorie: Prompt Engineering
Kategorie: Generative AI
Kategorie: AI Workflows
Kategorie: Agentic Workflows

Wichtige Details

Zertifikat zur Vorlage

Zu Ihrem LinkedIn-Profil hinzufügen

Bewertungen

14 Zuweisungen¹

KI-bewertet siehe Haftungsausschluss

Unterrichtet in Englisch

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Autonomous AI Agent Systems and Orchestration“

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Erwerben Sie ein Berufszertifikat zur Vorlage

In diesem Kurs gibt es 4 Module

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.

Through structured lessons, guided projects, and practical demonstrations, you’ll explore how to orchestrate agent interactions, assign tasks dynamically, and ensure system reliability as agent complexity increases. You’ll work with orchestration patterns and communication protocols that allow AI agents to reason collectively, respond to user input, and handle real-time decision-making. By the end of this course, you will be able to: • Understand orchestration frameworks and scaling strategies for AI agent systems. • Implement coordination and monitoring techniques for autonomous agent workflows. • Optimize task management, load balancing, and performance in multi-agent environments. • Design scalable customer support agents that handle queries, adapt behavior, and improve with feedback. This course is ideal for AI developers, data scientists, and automation engineers who want to build enterprise-ready AI systems that perform efficiently at scale. A basic understanding of Python programming and prior experience with AI or machine learning will be helpful but not mandatory. Join us to explore how orchestration and scaling turn simple AI agents into intelligent, autonomous systems capable of managing complex, real-world operations.

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.

Das ist alles enthalten

12 Videos5 Lektüren4 Aufgaben

12 VideosInsgesamt 62 Minuten

Specialization Introduction5 Minuten
Course Introduction3 Minuten
The Shift from Digital Collaboration to True Autonomy5 Minuten
Tool Mastery: LangGraph Defining the Graph State and Nodes5 Minuten
Hands-on: Creating the First LangGraph Flow and Executing It5 Minuten
Hands-on: Implementing the Tier-1 Triage Node and Conditional Routing6 Minuten
Tool Mastery: Vision LLMs Principles of Visual AI and Multimodal Reasoning5 Minuten
Hands-On: Using Vision LLMs to Extract Error Codes from Customer Screenshots5 Minuten
Hands-on: Integrating External API Tools6 Minuten
Hands-on: Building the Multi-Tool Orchestration Node: Sequential Diagnostic Logic5 Minuten
Hands-On: Setting up the Simulated Chat Interface Input/Output7 Minuten
Hands-on: The Visually-Informed Triage Agent4 Minuten

5 LektürenInsgesamt 50 Minuten

Course Outline: AI Agent Orchestration and Scaling10 Minuten
Foundations of LangGraph and State10 Minuten
Multimodal Input and Real-Time Diagnosis10 Minuten
Building the Agent10 Minuten
Multimodal Inputs and Stateful Orchestration10 Minuten

4 AufgabenInsgesamt 33 Minuten

Knowledge Check: Multimodal Inputs and Stateful Orchestration15 Minuten
Practice Quiz: Foundations of LangGraph and State6 Minuten
Practice Quiz: Multimodal Input and Real-Time Diagnosis6 Minuten
Practice Quiz: RAG for Financial Data6 Minuten

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.

Das ist alles enthalten

10 Videos4 Lektüren4 Aufgaben

10 VideosInsgesamt 53 Minuten

LTM Theory(Episodic vs. Semantic Memory in Autonomous Systems)5 Minuten
Tool Mastery (Knowledge Graphs - Modeling User & Error Relationships)5 Minuten
Hands-on: Writing New Experience and Querying Context5 Minuten
Tool Mastery(LlamaIndex Advanced Memory Modules - Consolidation Logic)5 Minuten
Hands-on: Implementing the LTM Check Node6 Minuten
Hands-on: The Planner Agent's Role5 Minuten
Hands-on: The Re-Planning Node Logic4 Minuten
Hands-on: Building the User Feedback Loop5 Minuten
Hands-on: Implementing Sub-Graphs for Nested Tasks7 Minuten
Hands-on: The Agent Demonstrates LTM-Informed Dynamic Re-Planning5 Minuten

4 LektürenInsgesamt 40 Minuten

LTM Architecture and Knowledge Graphs10 Minuten
Retrieval and Integration Logic10 Minuten
Self-Correction and Dynamic Flow10 Minuten
Long-Term Memory and Dynamic Re-Planning10 Minuten

4 AufgabenInsgesamt 33 Minuten

Knowledge Check: Long-Term Memory and Dynamic Re-Planning15 Minuten
Practice Quiz: LTM Architecture and Knowledge Graphs6 Minuten
Practice Quiz: Retrieval and Integration Logic 6 Minuten
Practice Quiz: Self-Correction and Dynamic Flow 6 Minuten

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.

Das ist alles enthalten

11 Videos4 Lektüren4 Aufgaben

11 VideosInsgesamt 57 Minuten

The Irreversible Action Guardrail: Preventing High-Risk Workflow Execution5 Minuten
Hands-on: Human-in-the-Loop Integrating a Manual Hand-off Node6 Minuten
Data Governance: Automating the Audit Trail and Decision Logging5 Minuten
Hands-on: Flask - Setting up the Agent’s API Service5 Minuten
Hands-on: Flask - Deploying the Full LangGraph Workflow for Production Access5 Minuten
Hands-on: Scaling Concepts: Edge Agents vs. Cloud Orchestration6 Minuten
Kubernetes/Containerization for Fleet Management5 Minuten
Hands-on: Connecting all Modules into the Support Agent6 Minuten
Hands-on: Testing and Validating the Autonomous Workflow6 Minuten
Hands-on: Integrating LangSmith for LLM Monitoring and Observability4 Minuten
The Future of Autonomous Process Optimization5 Minuten

4 LektürenInsgesamt 40 Minuten

Orchestration, Governance, and Scaling10 Minuten
Production Deployment and Scaling10 Minuten
Integration and Next Steps10 Minuten
Orchestration, Governance, and Scaling10 Minuten

4 AufgabenInsgesamt 33 Minuten

Knowledge Check: Orchestration, Governance, and Scaling15 Minuten
Practice Quiz: Governance and Guardrails6 Minuten
Practice Quiz: Production Deployment and Scaling 6 Minuten
Practice Quiz: Integration and Next Steps6 Minuten

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.

Das ist alles enthalten

1 Video1 Lektüre2 Aufgaben

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

197 Kurse178.983 Lernende

von

Edureka

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Warum entscheiden sich Menschen für Coursera für ihre Karriere?

Felipe M.

Lernender seit 2018

„Es ist eine großartige Erfahrung, in meinem eigenen Tempo zu lernen. Ich kann lernen, wenn ich Zeit und Nerven dazu habe.“

Jennifer J.

Lernender seit 2020

„Bei einem spannenden neuen Projekt konnte ich die neuen Kenntnisse und Kompetenzen aus den Kursen direkt bei der Arbeit anwenden.“

Larry W.

Lernender seit 2021

„Wenn mir Kurse zu Themen fehlen, die meine Universität nicht anbietet, ist Coursera mit die beste Alternative.“

Chaitanya A.

„Man lernt nicht nur, um bei der Arbeit besser zu werden. Es geht noch um viel mehr. Bei Coursera kann ich ohne Grenzen lernen.“

Häufig gestellte Fragen

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.

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.

Weitere Fragen

Besuchen Sie die das Hilfe-Center für Kursteilnehmer.

Finanzielle Unterstützung verfügbar,

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