Introduction
AI tools are conveniently used by everyone today, but the problem is that the extent of using AI is mostly limited to chatbots and simple automation.
You copy text from one tool, paste it into another, run the same steps again, and still don’t get reliable results. This is because normal AI tools cannot plan tasks or take action on their own.
Which isn’t wrong per se, but there are so many ways through which you can have AI do most of your tedious tasks, and this is exactly how AI agents can come in to help. And the best way to begin your learning is to look into that one course that can completely guide you through the process. But can it really help you?
Basically, an AI agents course teaches you how to build AI systems that can think step by step, use tools, call functions, and complete tasks without constant input from you. Instead of asking AI to help again and again, you learn how to make AI work for you automatically.
You can also check out: Agentic AI Self-Study Roadmap 2026
Terms like tool calling, function calling, or LLM agents can be new and confusing, but worry not, as with practice, all the terminologies and process becomes doable. Many learners struggle because they know how to prompt AI, but don’t know how to make it use tools, connect to data, or follow goals. A structured agentic AI course can help you solve this exact problem.
In the best ai agents courses, you will learn how tool-using agents work in projects. You’ll be able to understand how AI decides which tool to use and when to use it. A focused function calling course and tool calling training helps you build agents that can interact with APIs, files, browsers, and databases.
So, here we have prepared a list of 10 ai agent courses that can truly help you learn the skills you need!
What Are Tool-Using Agents?
Tool-using agents are AI programs that can use external tools while working on a task. When the task needs data or an action outside the model, the agent uses tools like APIs, databases, or internal systems instead of guessing or stopping at a text response.
This is different from a chatbot. A chatbot only replies to what you ask. A tool-using agent completes the task by fetching information, updating systems, and producing an outcome based on real data.
For example, a customer support agent receives a query. It fetches policy text using retrieval-based search, pulls order details through an API, drafts a reply, opens a support ticket, and logs every action taken during the process.
Hence, a tool-using agent is modelled to complete tasks at hand, whereas chatbots only answer queries.
What You’ll Learn in a Good AI Agents Course – 2026 Checklist
Here are some of the topics that you can look out for in a goo ai agents course:
- Tool and function calling with structured outputs (JSON schemas)
- Tool routing to select the right tool, handle retries, and use fallbacks
- Agent loop (plan – act – observe – reflect) for multi-step execution
- State and memory, including short-term context and long-term preferences
- Agentic RAG for retrieval, grounding, and citation-based responses
- Agent evaluation covering accuracy, grounding, latency, and cost
- Safety and guardrails, including refusals, permissions, and sensitive tools
Together, these skills form the core of most ai agents courses and agentic AI course curricula.
How We Ranked These AI Agents Courses
Not all AI agents courses focus on the same outcomes, so we ranked them using clear and practical criteria.
- Hands-on builds, with projects valued more than lectures
- Use of a modern agent stack, including tool calling, RAG, memory, and graphs
- Emphasis on reliability through testing, tracing, and evaluation
- Coverage of common frameworks such as LangChain agents, LangGraph, AutoGen, and CrewAI, where relevant
- Clear outcomes, showing who the course is for and what you will build
Using this approach, we were able to list down the best courses in accordance with the criteria, so if you wish to have a certain liking towards a concept/topic/methodology, or just the course as a whole, you’ll be able to check them through the descriptions for each of them.
Who This List Is For
This list is helpful for learners who want to move beyond basic prompting and start building AI automation agents.
- Developers building agentic workflows into product features
- Automation builders connecting APIs, web data, SQL, documents, or email tools
- ML and AI learners moving from prompting to deploying LLM agents
- Professionals who want a portfolio-ready agent project in less time
Each course listed is mapped to one or more of these goals.
Quick Note Before You Start
You don’t need advanced research experience, but a few basics will help you move faster.
- Comfortable with Python or JavaScript fundamentals
- Familiar with basic REST APIs
- Understanding of core LLM concepts like tokens, prompting, and embeddings
- Optional: basic ML foundations (helpful, not required)
With these in place, you’ll be better prepared to get value from an AI agents course or related ai agents courses.
Tp 10 AI Agents Courses to Learn Tool-Using Agents
We have divided all courses into 6 parts, each covering different outcomes, and based on these, you can select the course that can help you best!
Here is a table that you can check out before getting into the detailed list:
| Course | Best for | Key coverage | Proof of work |
| Scaler x IIT Roorkee AI Engineering Course | Professionals looking for a guided, job-ready path to build and ship agent-based applications | LLM foundations, tool and function calling, RAG systems, agentic workflows, and applied project building | Guided, hands-on program with portfolio-oriented builds delivered through an IIT Roorkee continuing education context |
| DeepLearning.AI – Functions, Tools, and Agents with LangChain | Builders who want a fast and focused introduction to tool calling and structured agent workflows | Tool calling, function calling, structured outputs, and composing agents using LangChain | Short course format with build-along exercises and completion certificate |
| Udacity – Building Agents | Developers aiming to build production-style agents with real APIs and evaluation | Function calling, API integration, agent state, memory handling, agentic RAG, and evaluation basics | Course completion with clearly defined outcomes and regularly updated content |
| DeepLearning.AI – AI Agents in LangGraph | Builders who need predictable control flow and stateful agent behavior | Graph-based agent design, state management, and multi-step workflows using LangGraph | Hands-on course focused on rebuilding agents with explicit control flow |
| DeepLearning.AI – Long-Term Agentic Memory with LangGraph | Learners building assistants that must remember preferences and remain consistent over time | Long-term memory patterns, preference handling, and context-aware agent behavior | Practical examples demonstrating memory-driven agent logic |
| DeepLearning.AI – AI Agentic Design Patterns with AutoGen | Builders interested in role-based, collaborative multi-agent systems | Multi-agent design, role separation, and agent-to-agent coordination using AutoGen | Applied design patterns demonstrated through structured agent teams |
| DeepLearning.AI – Multi AI Agent Systems with CrewAI | Learners building workflows that require delegation and coordination across agents | Team orchestration, task delegation, and multi-agent sequencing using CrewAI | Course completion with practical examples of coordinated agent systems |
| DeepLearning.AI – Building Agentic RAG with LlamaIndex | Builders creating agents that work over internal documents with grounding and citations | Agentic RAG, document reasoning, retrieval grounding, and citation-based responses | Hands-on builds demonstrating document-aware agent behavior |
| Hugging Face – AI Agents Course | Beginners to intermediate learners seeking a free and structured introduction to AI agents | Core agent concepts, guided units, and practical building blocks | Free certification with documented requirements inside the course |
| Microsoft – AI Agents for Beginners | Absolute beginners who want a lesson-based path from concepts to code | Agent fundamentals, structured lessons, and introductory implementations | Public GitHub repository with lesson-based structure and examples |
Now that we have summarized all the courses, let’s look further into their details!
Part 1: Best End-to-End Program – Job-Ready Tool-Using Agents
In this part, we have covered the ai agent course, which includes all the necessary components in one program, i.e, from core LLM concepts to building reliable, tool-using agents you can showcase in a portfolio.
1) Scaler x IIT Roorkee Advanced AI Engineering Course
This AI agents course is well-suited for learners who want one guided program that connects LLMs, tool and function calling, RAG systems, and agentic AI workflows into a single learning path. The focus stays on building systems that resemble real product features, not isolated demos.
This course can be best suited for you if you are a:
- Working professionals aiming to ship agent-based features or applications
- Engineers moving into AI product, platform, or agent-focused roles
- Builders who prefer structured learning with clear outcomes over scattered tutorials
Key coverage
- LLM and application foundations, moving from prompting to structured outputs
- Retrieval grounding using RAG systems for reliable, data-backed responses
- Agentic AI workflows where agents plan actions, use tools, and complete tasks
Proof of work
- Guided, hands-on course format with applied builds
- Delivered in partnership with IIT Roorkee through a continuing education program
- Emphasis on building deployable components rather than only theoretical coverage
Among current ai agents courses, this program offers one of the most complete end-to-end paths.
Also note:
- The program covers an end-to-end curriculum with modern agent stacks
- Strong focus on portfolio outcomes and applied learning
- Requires more time commitment than a short 2-10 hour course
You can build a project portfolio:
Build a support agent that retrieves internal documents using agentic RAG, calls tools for ticket creation, email, or calendar updates, and logs each run with a simple agent evaluation checklist.
Check out: Scaler x IIT Roorkee’s AI engineering with Gen AI and LLMs program for further details!
Part 2: Tool Calling + Structured Outputs – Core Skill for Agents
This part covers courses that focus on tool calling, function calling, and structured outputs, which form the base of most reliable AI agents and tool-using agents.
2) DeepLearning.AI – Functions, Tools, and Agents with LangChain
This course is a good introduction to tool calling and function calling using LangChain agents. It is designed to help learners understand how large language models interact with external tools through structured inputs and outputs.
The course walks through common LLM API patterns and shows how chains and agents are composed using LCEL. The emphasis stays on controlled interactions, which makes it suitable for learners building their first tool using agents.
- It covers tool calling and structured interaction patterns
- Introduces composing chains and agents with LangChain
- Uses short build-along exercises to reinforce concepts
You can extend the agent with agentic RAG, so it retrieves evidence first and calls tools only when required. Add basic agent evaluation to check tool accuracy and response grounding.
3) Udacity – Building Agents
Udacity’s course takes a more application-oriented approach to AI automation agents. It focuses on building agents that work with real APIs, manage state, and operate across multiple steps, including the edge cases that appear in production systems.
The course places attention on function calling, structured outputs, and maintaining agent state over time. It also introduces agentic RAG and evaluation concepts, which are important for building dependable agent workflows.
- The course teaches API integration through function calling and structured outputs.
- Covers agent state handling, including short-term and long-term memory
- Includes evaluation considerations for reliability and cost
You can also build a “Meeting Notes to Actions” agent that converts notes into tasks or tickets and drafts updates for stakeholders.
Part 3: Agent Control Flow – Reliable, Stateful Agents
As AI agents grow more complex, control flow and state management become critical. Courses in this section focus on building predictable, stateful agents that behave consistently across multi-step tasks.
4) DeepLearning.AI – AI Agents in LangGraph
This course is designed for learners who have already built basic agents but have a hard time growing reliability and control. It introduces LangGraph as a way to structure agent logic using explicit states and transitions.
The course walks through building an agent from scratch and then rebuilding it using graph-based control flow. With this approach, you can learn to manage complex, multi-step tasks where agents need to pause, resume, or branch based on intermediate results.
- It covers graph-based agent design using LangGraph
- Focuses on stateful workflows for predictable execution
- Well-suited for agents that plan, act, and verify before producing output
You can refactor an existing agent into a state machine with steps such as plan, search, verify, act, and report.
5) DeepLearning.AI – Long-Term Agentic Memory with LangGraph
This course focuses on one of the hardest problems in agentic AI, which is maintaining consistency over time. It explores how agents can store and retrieve long-term information while continuing to behave in a controlled and predictable way.
Using LangGraph, the course demonstrates memory patterns that allow agents to remember preferences, context, or past interactions. Examples include personal assistants that decide when to respond, ignore, or notify based on stored information.
- The course teaches long-term memory patterns using LangGraph
- Demonstrates preference-based and context-aware agent behavior
- Useful for building assistants that evolve over repeated interactions
You can try to introduce memory safety by storing only user-approved preferences and making stored memory visible and editable.
Part 4: Multi-Agent Systems – Teams of Agents
Some tasks are easier to solve when work is split across multiple agents, which becomes more useful than loading everything into one system. Courses in this section focus on multi-agent systems, where agents have clear roles and collaborate to complete complex workflows.
6) DeepLearning.AI – AI Agentic Design Patterns with AutoGen
This course introduces AutoGen as a framework for building systems where multiple agents work together. So here, instead of a single agent planning, researching, writing, and reviewing, responsibilities are separated across agents with defined roles.
The course focuses on designing agent-to-agent interactions and coordinating tasks across a team. This makes it useful for builders who want to create structured, role-based LLM agents rather than one large, overloaded agent.
- It covers multi-agent design using AutoGen.
- Emphasizes role separation, such as planner, researcher, writer, and reviewer
- Suitable for building collaborative AI automation agents
By the time you complete the course, you will be able to create a multi-agent pipeline where one agent reviews tool calls and citations before the final output is produced.
7) DeepLearning.AI – Multi AI Agent Systems with CrewAI
This course focuses on CrewAI, which is designed for organizing and coordinating agents in multi-step workflows. It is particularly useful when tasks need clear delegation, sequencing, and coordination across agents.
The course explains how to design effective agent roles and how to structure teams so that each agent handles a specific part of a larger task. This approach helps reduce errors and improve consistency in complex agent workflows.
- The course introduces principles for designing effective multi-agent teams
- Focuses on structured delegation and coordination using CrewAI
- Useful for workflows that require multiple agents to contribute in sequence
You can also build a research team with defined roles and a final aggregator agent that combines outputs into one cited response.
Part 5: Agentic RAG – Agents That Work Over Your Data
When agents work with internal data, accuracy and grounding matter more than speed. Courses in this section focus on agentic RAG, where agents retrieve information from documents, reason over it, and produce answers that are tied to verifiable sources.
8) DeepLearning.AI – Building Agentic RAG with LlamaIndex
This course is designed for builders creating agents that must work with private or internal data such as policies, knowledge bases, and PDFs. It uses LlamaIndex to show how agents retrieve relevant context before responding, reducing guesswork and hallucinations.
The course demonstrates how to build agents that reason over retrieved content rather than treating retrieval as a simple search step. This makes it especially useful for document-heavy use cases that require citations and traceable answers.
- It covers building agents that retrieve and reason over documents
- Emphasizes grounding responses with citations
- Suitable for document-based LLM agents and enterprise-style workflows
You can add agent evaluation to test whether the agent retrieved the correct chunks and cited them accurately before deploying the system.
Part 6: Best Free Starters – Beginner-Friendly
You are a beginner and don’t wish to start with a paid program? We totally understand. Courses in this section are free, structured, and beginner-friendly, making them good entry points into AI agents courses before moving to more advanced agentic AI topics.
9) Hugging Face – AI Agents Course
This course offers a structured and accessible introduction to AI agents, making it suitable for beginners and intermediate learners. It focuses on core agent concepts and gradually introduces practical building blocks without assuming deep prior experience.
The course is fully free and includes an optional certification, which can be useful for learners looking to validate their understanding while building foundational skills.
- The course includes guided units focused on agents and practical components.
- Clear progression from concepts to small implementations
- Certification requirements are clearly documented within the course
You can ship one complete agent project and add a LangGraph control-flow layer to improve reliability and execution clarity.
10) Microsoft – AI Agents for Beginners
This course is designed for absolute beginners who want a lesson-based introduction to AI agents. It follows a simple “concept to code” approach, helping learners understand ideas first and then apply them through small coding exercises.
The content is organized as a short, structured program that lowers the entry barrier for learners who are new to agentic AI and LLM agents.
- You’ll have a ten-lesson format covering the fundamentals of building AI agents.
- Focuses on clarity and step-by-step progression
- Backed by a public GitHub repository that is easy to explore and fork
After completing the course, take a tool calling or function calling course and build a small agent that uses one or two real tools.
How to Choose the Right AI Agents Course
Choosing the right AI agents course depends on what you want to build and how quickly you want to get there. Some courses focus on foundations, while others are better suited for production systems or advanced agent workflows. Use the guide below to match your goal with the right course.
- If you are looking for one guided, job-ready learning path that takes you from core LLM concepts to tool calling, RAG, and agentic workflows, the Scaler x IIT Roorkee Advanced AI Engineering Course can help you with it.
- If your main goal is to quickly understand tool calling and function calling and start composing agents with structured outputs, the DeepLearning.AI course on Functions, Tools,s and Agents with LangChain is a practical starting point.
- If you want a more production-oriented approach that covers real API integration, state handling, and evaluation, Udacity’s Building Agents course focuses on those aspects.
- If you already have basic agents working but want more predictability and cleaner control flow, the DeepLearning.AI course on AI Agents in LangGraph is designed for building stateful, reliable agent workflows.
- If your focus is on long-term memory and maintaining consistent agent behavior over time, the DeepLearning.AI course on Long-Term Agentic Memory with LangGraph explores memory patterns in detail.
- If you are interested in multi-agent systems where tasks are divided across roles like planning, research, and review, the DeepLearning.AI courses on Agentic Design Patterns with AutoGen and Multi AI Agent Systems with CrewAI are both relevant options.
- If you want to build agents that work over your own documents with grounding and citations, the DeepLearning.AI course on Building Agentic RAG with LlamaIndex is a good choice to go with.
- If you prefer to start with a free and beginner-friendly option, the Hugging Face AI Agents Course and Microsoft’s AI Agents for Beginners both offer structured entry points.
Portfolio Projects That Show Tool-Using Agent Skills
Portfolio projects work best when they show how an agent behaves in real situations. The ideas below focus on skills that matter in practice, such as tool selection, grounding, state handling, and evaluation.
- A Tool Router Agent that decides which tool to use based on the task. For example, the agent chooses between search, SQL, or email tools by first understanding the user’s intent and then calling the correct tool with structured inputs.
- A RAG Support Agent that retrieves policy or knowledge-base documents, cites the source for every answer, and clearly refuses to respond when supporting evidence is missing.
- An SQL Analyst Agent that understands database schemas, generates safe SQL queries, validates them before execution, and explains the results in plain language.
- A Multi-Agent Research Team where work is split across roles. One agent plans the task, another gathers information, a third drafts the response, and a reviewer agent checks accuracy and citations before final output.
- A Memory-Enabled Assistant that stores user preferences and past context, summarizes what it remembers, and allows users to control or edit stored memory.
- An Evaluation Harness that tests agent behavior by checking whether the correct tool was selected, whether responses are grounded in retrieved data, and how much time or cost each run requires.
For more project ideas that follow this structure, you can explore Top Generative AI Projects to Build in 2026 as a next step.
FAQs
What’s the difference between agentic AI and generative AI?
Generative AI focuses on producing content such as text, images, or code based on a prompt. It usually responds once and stops there.
Agentic AI is about executing tasks. An agent can plan steps, use tools, retrieve data, and complete a task end-to-end. In short, generative AI creates outputs, while agentic AI acts toward a goal.
Do I need LangChain or LangGraph to build agents?
You don’t necessarily need them, but they often make agent development much easier. LangChain helps with tool calling, structured outputs, and composing agents, while LangGraph is useful when you need clear control flow, state, and reliability. For simple agents, basic APIs may be enough. For production-style agents, these frameworks save time and reduce errors.
How do tool- and function-calling agents work in real apps?
In applications, tool- and function-calling agents connect AI models to external systems such as APIs, databases, or internal services. The agent calls a function with structured inputs, receives real data, and uses it to complete the task. This is how agents check order status, send emails, query databases, or update records in live systems.
What is agentic RAG, and when should I use it?
Agentic RAG combines retrieval with decision-making. Instead of only fetching documents and answering, the agent decides when to retrieve, what to retrieve, and how to use the retrieved data before taking action. You should use agentic RAG when your agent depends on internal documents, policies, or knowledge bases and needs grounding, citations, or traceability.
How do I evaluate whether an agent is reliable?
Agent reliability is evaluated by checking three things.
- Whether the agent selects the correct tool or data source.
- Whether its responses are grounded in retrieved or verified information.
- Whether it performs efficiently in terms of time and cost.
Simple evaluation setups often include test cases, logs of tool calls, and checks for consistency across runs.
What projects impress recruiters for agent engineer roles?
Recruiters mainly look for abilities, like decision-making and critical thinking. Providing prompts is great, but the thought process behind certain decisions is what they are interested in knowing. For example, agents that route tasks to the right tools, work over documents using RAG with citations, manage state or memory, or coordinate multiple agents with clear roles. Projects that include evaluation, logging, and clear explanations of design choices often leave the most.