Advance Multi-Agent Systems & Production AI with LangSmith | Board Infinity
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Intermediate Course 3 of 3 · AI Agents: From Foundations to Applications

Build AI agents that can collaborate, use tools, and scale.

Move from single-agent experiments to production-ready multi-agent AI systems - with structured tool calling, coordinated agents, MCP and the guardrails production needs.

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Intermediate 4 modules · 16 assignments 2 weeks · 10 hours/week Self-paced
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Board Infinity Board InfinityInstructor · 287 courses on Coursera
Introduction to the course · 4:28

Demos in every module

Tool calling, CrewAI, an MCP server and production memory

Certificate included

Shareable on LinkedIn the day you finish

Checked as you go

16 assignments across the four modules

6 Lakh+learners on our Coursera courses
10+domains covered
270+courses
57+specializations
01 About the course

From one agent to a production-ready system.

Four modules, in the order the problems appear: make tools reliable, make agents work together, standardize the tools, then ship it safely.

4
Modules
Tool calling to scale
35
Video lessons
Short, demo-led lessons
16
Assignments
4 graded quizzes, 12 practice
16h
Total learning time
≈ 2 weeks at 10 hours a week

Advance Multi-Agent Systems & Production AI with LangSmith takes you past the single agent. It starts by treating every tool as a contract - API contracts, schema validation and safe execution wrappers, with agents run as execution loops and debugged with traces.

From there you coordinate specialized agents that delegate, communicate and collaborate (including CrewAI), standardize tools with Model Context Protocol by building an MCP server with FastAPI, and finally take the system to production: persistent memory on DynamoDB + ChromaDB, safety guardrails and red-teaming, monitoring and scaling.

It's the final course of Board Infinity's AI Agents: From Foundations to Applications specialization on Coursera - 35 videos, 12 readings and 16 assignments.

Inside the course
01Tool-calling & function execution≈ 4h 02Multi-agent architectures & coordination≈ 4h 03Model Context Protocol & tool integration≈ 4h 04Production, evaluation & scaling≈ 4h
02 Who it's for

For builders ready to go past the single agent.

An intermediate course. It fits best if you already understand what an LLM agent is and want it to do more.

AI developers

From one LLM app to many agents

Moving from individual LLM applications toward orchestrated agent systems.

AI engineers

Tool calling to deployment

Interested in tool calling, multi-agent coordination and production deployment.

Technical architects

Systems, not single prompts

Focused on architecture, communication, orchestration and scalability.

Workflow builders

Agents wired to real tools

Developers who want to connect agents with tools, APIs and MCP.

GenAI practitioners

The next step after foundations

Progressing from foundational agent concepts toward advanced workflows.

03 What you'll learn

Four capabilities, in the order they build.

Each module solves the problem the previous one exposes - from one reliable tool call to a system that keeps running.

Module 1

Tool calling

Implement tool-calling mechanisms using LLM function interfaces - API contracts, schemas and safe execution.

Module 2

Multi-agent coordination

Construct multi-agent workflows with task delegation and communication.

Module 3

MCP integration

Explain the principles of MCP and its role in standardizing agent tools - then build and consume them.

Module 4

Production AI

Deploy agent systems with memory persistence and safety guardrails, then monitor and scale them.

1:1 coaching · optional add-on

Build with someone in your corner.

Board Infinity 1:1 coaching pairs you with a practitioner while you take the course. It's a separate, optional service - not part of Coursera enrollment.

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Clarify difficult concepts

Talk through the parts that don't click on the first pass.

Review your practice

Feedback on what you build as you work through the modules.

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Discuss where to take your learning next.

Prepare for technical discussions

Practise explaining your design choices out loud.

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04 Syllabus

Four modules, from tool calling to scale.

2 weeks at 10 hours a week, self-paced - each module listed at 4 hours. Every module lists its learning objectives, its content, and what you'll walk out with.

35 videos 12 readings 16 assignments ≈ 16 hours Download full syllabus
Learning objectives - by the end of this module you can:
  • Implement tool-calling mechanisms using LLM function interfaces
  • Treat tools as API contracts, and explain why schema validation is mandatory
  • Wrap tool calls in a safe execution wrapper and run agents as execution loops
  • Debug agent runs with traces, chain tools, and integrate external APIs
What's inside
9 videos45 min
3 readings90 min
4 assignments105 min
1 plugin5 min
Topics covered
What LLMs should and shouldn't do Tools as API contracts Schema validation Safe execution wrapper Agents as execution loops Live execution demo Debugging with traces Tool chaining External APIs
You'll leave with: how agents call tools through API contracts, schema validation and safe execution wrappers, and how to debug runs with traces.
Learning objectives - by the end of this module you can:
  • Construct multi-agent workflows with task delegation and communication
  • Handle failure in chained agents with retries and guardrails
  • Explain why single agents break at scale, and define agent roles and messaging
  • Build a multi-agent research summarization workflow with CrewAI
What's inside
9 videos40 min
3 readings90 min
4 assignments105 min
Topics covered
Chained agent execution Failure, retries & guardrails Multi-agent coordination Why single agents break Research agents case study Agent roles & messaging CrewAI Research summarization demo
You'll leave with: how specialized agents delegate, communicate and collaborate, from chained execution to a CrewAI research workflow.
Learning objectives - by the end of this module you can:
  • Explain the principles of MCP and its role in standardizing agent tools
  • Describe MCP's core components - schemas, endpoints and contracts
  • Create an MCP server with FastAPI, and register, discover and test its tools
  • Integrate MCP clients with existing agents and chain MCP tools for multi-step tasks
What's inside
9 videos36 min
3 readings90 min
4 assignments105 min
Topics covered
Introduction to MCP Agent interoperability Schemas, endpoints & contracts MCP server with FastAPI Schema registration & discovery Testing MCP endpoints MCP clients Chaining MCP tools Web search + summarization demo
You'll leave with: how MCP standardizes agent tools, from schemas and an MCP server built with FastAPI to MCP clients and chained tools.
Learning objectives - by the end of this module you can:
  • Deploy agent systems with memory persistence and safety guardrails
  • Implement persistent memory with DynamoDB and ChromaDB
  • Build safety guardrails with prompt filters and red-teaming, and evaluate the system
  • Monitor in production and choose between horizontal and vertical scaling
What's inside
8 videos51 min
3 readings90 min
4 assignments105 min
Topics covered
AWS production Deployment & persistence DynamoDB + ChromaDB memory Evaluation Safety guardrails Prompt filters Red-teaming Monitoring in production Horizontal vs vertical scaling
You'll leave with: the production picture - deployment and persistent memory, safety guardrails and red-teaming, monitoring and scaling choices.
05 Tools & skills

The tools you work with, and the skills you leave with.

The technologies the lessons use, module by module. Coursera's official tool list includes Model Context Protocol, LangChain, vector databases, model deployment and AI orchestration.

Module 1

Tool schemas & JSON

Tools as API contracts, validated before execution and wrapped so failures are handled.

Module 2

CrewAI

What it is, a minimal working example, and a multi-agent research summarization demo.

Module 3

MCP & FastAPI

An MCP server built with FastAPI, MCP clients plugged into existing agents.

Module 4

AWS, DynamoDB & ChromaDB

AWS production for agent systems, with persistent memory on DynamoDB + ChromaDB.

Skills you leave with

Skills as listed on Coursera, grouped the way the course splits them - building agents, running them in production, and the LLM foundations underneath.

Agents & tools

Getting agents to act and work together.

  • Generative AI Agents
  • Agentic systems
  • Tool Calling
  • AI Integrations

Production

Keeping a system running and growing.

  • Debugging
  • System Monitoring
  • Scalability
  • Context Management

LLM foundations

The model layer underneath it all.

  • LLM Application
  • Retrieval-Augmented Generation
  • Large Language Modeling
  • Embeddings
Certificate

Show you can think beyond single-agent AI.

Complete the course on Coursera to earn a shareable certificate from Board Infinity - with a verification link and a one-click path onto your LinkedIn profile.

Not an official LangSmith, LangChain, MCP, AWS or CrewAI certification, and not an industry license.

On your profile in one click

Add it to your LinkedIn profile, resume or CV, where recruiters actually look.

Anyone can verify it

Its own verification link, so a hiring manager confirms it in seconds.

Earned, not attended

Awarded on a graded quiz in each of the four modules - it certifies what you can do.

Completes the specialization

Course 3 of 3 in AI Agents: From Foundations to Applications.

06 Career impact

Capabilities teams building agent systems need.

Role areas related to the skills covered - not guaranteed job outcomes. Completing the course doesn't guarantee a role, an interview or a salary outcome.

Where these skills count

AI Engineer

Tool calling, agent loops and deployment.

Generative AI Developer

Agents that use tools, APIs and MCP.

AI Solutions Engineer

Connecting agents to existing systems.

LLM Application Developer

Reliable interfaces, traces and debugging.

AI Systems Engineer

Persistence, monitoring and scaling.

AI Architect

Multi-agent architecture and safety.

The honest comparison

Plenty of this is online. So what does a course add?

Free tutorials are a great way to learn individual pieces. What a structured course adds is the order, the connections between the pieces, and checking along the way.

What you get
This course
Scattered tutorials
ProgressionTool calling → coordination → MCP → production
One connected path from single agent to system
Isolated concepts, unclear progression
TechnologiesConnected into one system
CrewAI, MCP with FastAPI, DynamoDB + ChromaDB in one sequence
Hard to connect into one system
Production topicsWhat happens after the demo
Persistence, safety, evaluation, monitoring, scaling
Often stops at the demo
CheckingLearning that sticks
16 assessments across four modules
Little structured evaluation
ProofVerifiable, on LinkedIn
Shareable certificate through Coursera
Usually none
07 The bigger path

The final step of a three-course path.

This is Course 3 of 3 in AI Agents: From Foundations to Applications. Here is how it connects.

Course 1

Agent Foundations and Prompt Engineering

Agent architectures, prompt engineering, prompt chaining with Python, and research automation.

≈ 21 hours
View course
Course 2

RAG Systems & Agentic Workflows with Pinecone and LangGraph

RAG, vector search and agentic workflows.

≈ 20 hours
View course
You're here Course 3

Advance Multi-Agent Systems & Production AI with LangSmith

Tool calling, multi-agent coordination, MCP and production AI.

≈ 16 hours · 4 modules
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Know exactly what you're signing up for.

4 modules · 35 videos · 16 assignments · free PDF

08 FAQ

Common questions

Course answers come from the module curriculum. Enrollment answers are Coursera's own wording.

About the course

In this course, it means workflows where specialized agents delegate tasks, communicate and collaborate, instead of one agent doing everything. Module 2 covers multi-agent architectures and coordination, including why single agents break at scale.

Module 1 covers tool-calling mechanisms using LLM function interfaces: tools as API contracts, why schema validation is mandatory, a safe tools execution wrapper, agents as execution loops, debugging with traces, tool chaining and integrating external APIs.

Yes. Module 3 is Model Context Protocol (MCP) and Tool Integration: core components (schemas, endpoints and contracts), creating an MCP server using FastAPI, schema registration and discovery, integrating MCP clients with existing agents and chaining MCP tools for multi-step tasks.

CrewAI appears in Module 2, with lessons on what it is, a minimal working example, and a demo of a multi-agent workflow for research summarization.

Module 4 covers AWS production, evaluation and scaling for agent systems, deployment and persistence, and implementing persistent memory with DynamoDB + ChromaDB.

Yes, in two places. Module 2 covers failure, retries and guardrails in chained agents. Module 4 covers evaluation and safety guardrails, including building prompt filters and red-teaming.

Yes. Module 4 includes scaling and monitoring in production, and scaling architectures - horizontal vs vertical.

Coursera lists the course as Intermediate level, with recommended experience, and 2 weeks to complete at 10 hours a week on a flexible schedule. Each of the four modules is listed as 4 hours.

Enrollment & access

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.

Source: Coursera course page.

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.

Source: Coursera course page.

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.

Source: Coursera course page.

Want someone building this with you?

Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.

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Build beyond the single agent.

Move from tool calling and multi-agent coordination to MCP integration, production deployment, safety, evaluation and scaling.

Enroll on Coursera

Advance Multi-Agent Systems & Production AI with LangSmith · by Board Infinity · offered on Coursera

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