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

Ce cours fait partie de Spécialisation "Autonomous AI Agent Systems and Orchestration"

Instructeur : Edureka

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Avancées

Expérience recommandée

8 heures à compléter

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Avancées

Expérience recommandée

8 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

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

Outils que vous découvrirez

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

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

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Enseigné en Anglais

91%

of learners achieved a positive career outcome

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Ce cours fait partie de la Spécialisation "Autonomous AI Agent Systems and Orchestration"

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Il y a 4 modules dans ce cours

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.

Inclus

12 vidéos5 lectures4 devoirs

12 vidéosTotal 62 minutes

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

5 lecturesTotal 50 minutes

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

4 devoirsTotal 33 minutes

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

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.

Inclus

10 vidéos4 lectures4 devoirs

10 vidéosTotal 53 minutes

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

4 lecturesTotal 40 minutes

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

4 devoirsTotal 33 minutes

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

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.

Inclus

11 vidéos4 lectures4 devoirs

11 vidéosTotal 57 minutes

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

4 lecturesTotal 40 minutes

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

4 devoirsTotal 33 minutes

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

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.

Inclus

1 vidéo1 lecture2 devoirs

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Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

Edureka

197 Cours178 983 apprenants

Offert par

Edureka

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Foire Aux 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.

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.

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