Deploying AI Agents: LLMs, LangGraph, and Production APIs
Integrate LLMs into modular LangGraph workflows, validate structured outputs with Pydantic-AI, deploy persistent agents behind FastAPI with Mem0 and vector stores - then evaluate whether they are ready for production.
Deploying AI Agents: LLMs, LangGraph, and Production APIs
By Board Infinity · Offered on Coursera
- 27 video lessons · 17 assignments
- Intermediate level · Course 3 of 3
- Shareable certificate on completion
- Flexible schedule - learn at your own pace
LLM-powered reasoning pipelines
OpenAI and Anthropic models inside LangGraph nodes and control flow
Structured, validated outputs
Pydantic-AI schemas enforce reliable agent I/O
Deployed, persistent and evaluated
FastAPI with Mem0 and vector stores, then load tests and benchmarks
From agent prototypes to production APIs.
The course takes AI agent skills into production - building, validating and deploying LLM-powered agents, then measuring how well they hold up.
Module 1 integrates LLMs from OpenAI and Anthropic into LangGraph reasoning pipelines - designing reasoning nodes and control flow, configuring model parameters and token management, with iterative testing and refinement.
Module 2 enforces structure with Pydantic-AI - schema enforcement and structured outputs - and builds a Business Workflow Assistant with validated, reliable I/O.
Module 3 covers deployment: FastAPI backends, persistent memory with Mem0 and vector stores, and orchestration with Agno and CrewAI. Module 4 is evaluation - metrics, logging, load testing and benchmarking - and compares LangGraph, CrewAI and Agno to justify production choices.
For builders taking agents past the prototype.
Coursera lists this as an intermediate course with recommended experience. It is the final course of a specialization that moves from single-agent architecture and multi-agent collaboration into deployment.
Developers deploying agents
You have a working agent prototype and need it validated, served through an API and measured.
Developers integrating LLMs
OpenAI and Anthropic models wired into structured LangGraph reasoning workflows.
LangGraph and API-based agents
FastAPI inference endpoints, agent runtimes and orchestration with Agno and CrewAI.
Memory and validated outputs
Practitioners who need Pydantic-AI schemas plus Mem0 and vector-store persistence.
Three outcomes, in the order they build.
The three learning outcomes Coursera lists for this course - plus the applied project that pulls them together.
Integrate LLMs into LangGraph pipelines
Integrate LLMs into modular LangGraph pipelines with efficient token and prompt management.
Validate structured I/O with Pydantic-AI
Validate structured agent I/O using Pydantic-AI schemas for enterprise reliability.
Deploy persistent agents
Deploy agents with FastAPI, Mem0, and vector stores for persistent state.
Business Workflow Assistant
Module 2 builds a Business Workflow Assistant with validated, reliable I/O. It is applied project work, not a separate learning outcome.
Ship your agents with someone in your corner.
Board Infinity 1:1 coaching is a separate, optional service - not part of the Coursera course. See the coaching page for how it works.
Book a demo sessionCoaching is independent of Coursera enrollment, grading and certificates.
LangGraph control flow
Talk through reasoning nodes and control flow with someone else.
Schema design
Structuring Pydantic-AI schemas for your own agents.
Deployment choices
FastAPI, memory and orchestration options, discussed one to one.
What to learn next
Planning your own path after the specialization.
Four modules, from LLM pipelines to production evaluation.
Two weeks at 10 hours a week, fully self-paced. Every module lists its description, every lesson with its duration, and the topics it covers.
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.
- 1The Rise of Multi-Agent Collaboration5 minutes
- 2Industry Trends in Distributed Intelligence5 minutes
- 3Skills and Frameworks in Demand5 minutes
- 4How LLM APIs Integrate with LangGraph3 minutes
- 5Configuring Model Parameters for Reasoning5 minutes
- 6Designing Reasoning Nodes in LangGraph3 minutes
- 7Implementing Control Flow Between Nodes3 minutes
- 8Balancing Independence and Oversight3 minutes
- 9Creating a Multi-Node Reasoning Pipeline4 minutes
- 10Iterative Testing and Refinement3 minutes
- 1Career Scope in AI Agent Deployment15 minutes
- 2Fundamentals of LLM Integration15 minutes
- 3LangGraph Node Design for Reasoning15 minutes
- 4Building an Intelligent Workflow15 minutes
- 1Career Scope in Multi-Agent Systems30 minutes
- 2From Single to Multi-Agent Systems30 minutes
- 3Multi-Agent Roles and Responsibilities30 minutes
- 4Introduction to CrewAI30 minutes
- 5Foundations 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.
- 1Introduction to Data Validation5 minutes
- 2Handling Validation Errors3 minutes
- 3Implementing the Researcher-Writer-Editor Chain3 minutes
- 4Managing Inter-Agent Feedback Loops4 minutes
- 5Evaluating Reports and Output Reliability3 minutes
- 1Schema Enforcement with Pydantic‑AI15 minutes
- 2Implementing Structured Outputs15 minutes
- 3Building the Business Workflow Assistant15 minutes
- 1Planning Collaborative Workflows30 minutes
- 2Building the Content Team30 minutes
- 3Analyzing Team Performance30 minutes
- 4Designing 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.
- 1FastAPI Setup for Agentic Workflows3 minutes
- 2Creating Inference Endpoints3 minutes
- 3Configuring Mem0 for State Management6 minutes
- 4Persistent Memory Patterns for Agent Deployments.4 minutes
- 5Combining Multiple Runtimes5 minutes
- 6Task Collaboration in Production4 minutes
- 7Deployment Walkthrough4 minutes
- 1Building a FastAPI Agent Backend15 minutes
- 2Persistence with Mem0 and Vector Stores30 minutes
- 3Orchestration with Agno and CrewAI15 minutes
- 1Understanding Shared and Private Memory30 minutes
- 2Implementing Shared Memory with Mem030 minutes
- 3Debugging and Optimizing Memory Coordination30 minutes
- 4Shared 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.
- 1Implementing Performance Benchmarks3 minutes
- 2Analyzing Test Results and Bottlenecks3 minutes
- 3Benchmarking Agent Reliability4 minutes
- 4Comparative Analysis LangGraph vs CrewAI vs Agno4 minutes
- 5Choosing the Right Framework for Production4 minutes
- 1Building an Evaluation Pipeline15 minutes
- 2Reliability and Performance Testing15 minutes
- 3Framework Maturity and Reflection15 minutes
- 1Custom Orchestration with Agno30 minutes
- 2Case Study - Customer Support Workflow30 minutes
- 3Framework Benchmarking and Reflection30 minutes
- 4Orchestrating and Evaluating Multi-Agent Systems60 minutes
Module 1 is listed as 5 hours to complete while its description calls it a 4-hour module; both are shown as Coursera displays them. The specialization page lists this course at 16 hours. The Coursera course page is always the authoritative version.
The stack you work in, and the skills you leave with.
Four layers carry the production workflow. Coursera's official tools and skills are listed separately below.
LangGraph
Reasoning pipelines with nodes and control flow. Also a Coursera-listed tool.
Pydantic-AI
Schema enforcement and structured outputs for reliable agent I/O.
FastAPI
Agent backends and inference endpoints for deployment.
Mem0 & vector stores
Persistent memory and state management for deployed agents.
Coursera's official lists are kept apart from the wider stack the course description and lessons discuss.
Tools you'll learn
As listed on the Coursera course page.
- Vector Databases
- LangChain
- AI Orchestration
- Agentic Workflows
- CrewAI
- AI Workflows
- LangGraph
Skills you'll gain
Coursera's full list, including "Show all".
- Artificial Intelligence and Machine Learning (AI/ML)
- Data Validation
- LLM Application
- Performance Testing
- Agentic systems
- Context Management
- Application Deployment
- Memory Management
Also in the course
From the description and lessons - not Coursera's official tools list.
- Pydantic-AI
- Mem0
- Agno
- OpenAI and Anthropic integrations
- FastAPI
- Vector stores
- Structured I/O and schema validation
- Token and prompt management
- Logging, load testing and benchmarking
Proof you can link to.
Coursera lists a shareable certificate for this course - and you can add the credential to your LinkedIn profile.
A Coursera course certificate from Board Infinity - not a university degree, university credit or a regulated professional qualification.
On your LinkedIn profile
Add this credential to your LinkedIn profile.
Resume or CV
Add it to your resume or CV.
Earned through assessment
Across the 17 assignments in four modules.
Two names on it
Board Infinity as the provider, Coursera as the platform.
Agents that run in production - and where that skill applies.
Potential directions where the course's skills apply. They are areas to explore, not guaranteed job outcomes.
AI agent development
LangGraph reasoning pipelines with nodes and control flow.
LLM application engineering
OpenAI and Anthropic integration with token and prompt management.
Agent deployment
FastAPI backends, inference endpoints and persistent state with Mem0.
AI workflow engineering
Validated, structured workflows such as a Business Workflow Assistant.
API and systems integration
Serving agents through APIs and combining multiple runtimes in production.
MLOps and agent evaluation
Logging, load testing, reliability metrics and benchmarking.
Where this course sits in the specialization
This is the deployment and evaluation stage. The earlier courses focus on single-agent architecture and multi-agent collaboration; this one takes those agents into production with the frameworks below. It does not try to cover every production platform, LLM provider or infrastructure pattern.
Course 3 of 3. Here is the full path.
Building and Deploying AI Agents with LLMs and LangChain runs from single-agent architecture, to multi-agent collaboration, to production deployment.
AI Agent Architecture: Reasoning, Memory, and LangGraph
Modular agent architecture, structured validation and persistent memory.
View course Course 2 · 16 hoursDesigning Multi-Agent Systems: Collaboration and Workflows
CrewAI and Agno, role-based collaboration and shared memory.
View course Course 3 · You are hereDeploying AI Agents: LLMs, LangGraph, and Production APIs
LLM integration, validation, FastAPI deployment, persistence and evaluation. 16 hours.
View on CourseraCourse hours are from the specialization page (12 weeks at 4 hours a week overall) - see the full specialization.
Know exactly what you're signing up for.
Common questions
Everything learners ask before enrolling, answered from the Coursera course page.
It covers integrating LLMs into LangGraph pipelines, validating structured agent outputs with Pydantic-AI, deploying persistent agents through FastAPI with Mem0 and vector stores, and evaluating production workflows with logging, load testing and benchmarking.
The course lists Vector Databases, LangChain, AI Orchestration, Agentic Workflows, CrewAI, AI Workflows and LangGraph as tools. The course description also discusses Pydantic-AI, Mem0, Agno, FastAPI and integrations with OpenAI and Anthropic models.
Coursera lists this as an intermediate course with recommended experience; the course page does not publish a formal prerequisite checklist. It is Course 3 of 3 in a specialization whose earlier courses cover single-agent architecture and multi-agent collaboration.
Coursera estimates 2 weeks to complete at 10 hours a week, on a flexible schedule. The specialization page lists the course at 16 hours. In the module listings, Module 1 shows 5 hours to complete while its description calls it a 4-hour module.
Module 2 builds a Business Workflow Assistant with validated, reliable I/O, and Module 4 includes the assignment "Case Study - Customer Support Workflow".
To access course materials, assignments, and earn a Certificate, Coursera says you need to purchase the Certificate experience when you enroll. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option, which lets you access course materials, submit required assessments and receive a final grade, but not earn or purchase a Certificate.
When you enroll, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate is added to your Accomplishments page, where you can print it or add it to your LinkedIn profile.
Coursera lists a shareable certificate for this course that you can add to your LinkedIn profile. It is not a university degree, university credit or a regulated professional qualification.
Coursera says that in select learning programs you can apply for financial aid or a scholarship if you cannot afford the enrollment fee. If it is available for your selection, you will find a link to apply on the description page.
Want someone building this with you?
Board Infinity 1:1 coaching is a separate, optional offering - not part of the Coursera course.
Take your agents into production.
LangGraph pipelines, Pydantic-AI validation, FastAPI deployment and production evaluation in four self-paced modules. Enrollment, lessons, grading and the certificate all happen on Coursera.
Enroll on Coursera