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Deploying AI Agents: LLMs, LangGraph, and Production APIs

Deploying AI Agents: LLMs, LangGraph, and Production APIs

本课程是 Building and Deploying AI Agents with LLMs and LangChain 专项课程的一部分

位教师：Board Infinity

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Integrate LLMs into modular LangGraph pipelines with efficient token and prompt management
Validate structured agent I/O using Pydantic-AI schemas for enterprise reliability
Deploy agents with FastAPI, Mem0, and vector stores for persistent state

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Building and Deploying AI Agents with LLMs and LangChain 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

"Take your AI agent skills into production with this hands-on course on building, validating, and deploying LLM-powered agents using LangGraph, LangChain, Pydantic-AI, Mem0, CrewAI, Agno, and FastAPI. You’ll learn to turn prototypes into reliable, enterprise-grade agent systems.

Module 1 covers integrating LLMs (OpenAI, Anthropic) into LangGraph reasoning pipelines, designing nodes, control flow, token management, and iterative workflow testing. Module 2 focuses on schema enforcement with Pydantic-AI, structured outputs, and building a Business Workflow Assistant with validated, reliable I/O. Module 3 guides you through full deployment — FastAPI backends, persistent memory with Mem0 and vector stores, and orchestration with Agno and CrewAI in production. Module 4 teaches evaluation: metrics, logging, load testing, benchmarking, and comparing LangGraph, CrewAI, and Agno for enterprise-scale deployment. By the end of this course, you will: - Integrate LLMs into modular LangGraph reasoning pipelines - Validate agent I/O using Pydantic-AI schemas for reliable outputs - Deploy agents via FastAPI with Mem0 and vector-store persistence - Evaluate and benchmark frameworks to justify production choices"

单元详情

This 4-hour module introduces learners to the transition from single-agent to collaborative multi-agent systems, emphasizing teamwork dynamics, communication strategies, and distributed reasoning.

涵盖的内容

10个视频4篇阅读材料5个作业

10个视频总计39分钟

The Rise of Multi-Agent Collaboration5分钟
Industry Trends in Distributed Intelligence5分钟
Skills and Frameworks in Demand5分钟
How LLM APIs Integrate with LangGraph3分钟
Configuring Model Parameters for Reasoning5分钟
Designing Reasoning Nodes in LangGraph3分钟
Implementing Control Flow Between Nodes3分钟
Balancing Independence and Oversight3分钟
Creating a Multi-Node Reasoning Pipeline4分钟
Iterative Testing and Refinement3分钟

4篇阅读材料总计60分钟

Career Scope in AI Agent Deployment15分钟
Fundamentals of LLM Integration15分钟
LangGraph Node Design for Reasoning15分钟
Building an Intelligent Workflow15分钟

5个作业总计180分钟

Career Scope in Multi-Agent Systems30分钟
From Single to Multi-Agent Systems30分钟
Multi-Agent Roles and Responsibilities30分钟
Introduction to CrewAI30分钟
Foundations of Multi-Agent Collaboration60分钟

This 4-hour module has learners design and simulate a functional, role-based workflow demonstrating structured collaboration between multiple agents using CrewAI's orchestration tools.

涵盖的内容

5个视频3篇阅读材料4个作业

5个视频总计17分钟

Introduction to Data Validation5分钟
Handling Validation Errors3分钟
Implementing the Researcher-Writer-Editor Chain3分钟
Managing Inter-Agent Feedback Loops4分钟
Evaluating Reports and Output Reliability3分钟

3篇阅读材料总计45分钟

Schema Enforcement with Pydantic‑AI15分钟
Implementing Structured Outputs15分钟
Building the Business Workflow Assistant15分钟

4个作业总计150分钟

Planning Collaborative Workflows30分钟
Building the Content Team30分钟
Analyzing Team Performance30分钟
Designing Role-Based Multi-Agent Workflows60分钟

This 4-hour module explores shared memory integration in multi-agent systems, focusing on context continuity, communication efficiency, and memory optimization strategies using Mem0.

涵盖的内容

7个视频3篇阅读材料4个作业

7个视频总计29分钟

FastAPI Setup for Agentic Workflows3分钟
Creating Inference Endpoints3分钟
Configuring Mem0 for State Management6分钟
Persistent Memory Patterns for Agent Deployments.4分钟
Combining Multiple Runtimes5分钟
Task Collaboration in Production4分钟
Deployment Walkthrough4分钟

3篇阅读材料总计60分钟

Building a FastAPI Agent Backend15分钟
Persistence with Mem0 and Vector Stores30分钟
Orchestration with Agno and CrewAI15分钟

4个作业总计150分钟

Understanding Shared and Private Memory30分钟
Implementing Shared Memory with Mem030分钟
Debugging and Optimizing Memory Coordination30分钟
Shared Memory and Context Coordination60分钟

In this final 4-hour module, learners orchestrate multi-agent collaboration using Agno, simulate a real-world Customer Support workflow, and conduct comparative evaluations of leading frameworks.