Deploying AI Agents: LLMs, LangGraph, and Production APIs

Deploying AI Agents: LLMs, LangGraph, and Production APIs

This course is part of Building and Deploying AI Agents with LLMs and LangChain Specialization

Instructor: Board Infinity

Access provided by African Leadership University

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

17 assignments

Taught in English

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This course is part of the Building and Deploying AI Agents with LLMs and LangChain Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

"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"

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.

What's included

10 videos4 readings5 assignments

10 videosTotal 39 minutes

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

4 readingsTotal 60 minutes

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

5 assignmentsTotal 180 minutes

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

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.

What's included

5 videos3 readings4 assignments

5 videosTotal 17 minutes

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

3 readingsTotal 45 minutes

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

4 assignmentsTotal 150 minutes

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

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

What's included

7 videos3 readings4 assignments

7 videosTotal 29 minutes

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

3 readingsTotal 60 minutes

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

4 assignmentsTotal 150 minutes

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

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.