[0:00] Today, we're diving deep into one of the
[0:02] hottest topics in AI right now. Building
[0:05] actual agents in Python. Not just
[0:07] chatbots to respond to your queries, but
[0:10] autonomous systems with memory, goals,
[0:12] and the ability to take actions in the
[0:14] world. We're talking about personal
[0:16] assistants that schedule your meetings,
[0:18] research bots that gather information,
[0:21] development tools that can fix your
[0:22] code, web scrapers that can gather data,
[0:25] and much, much more. Now, this will not
[0:27] be a step-by-step coding tutorial.
[0:30] Instead, I'm going to give you a
[0:31] strategic overview of the landscape so
[0:33] you can choose the right tools for your
[0:35] specific project. But before we jump in,
[0:38] I want to give a quick thank you to
[0:39] Nvidia for sponsoring this video. Nvidia
[0:42] just launched two brand new
[0:44] certifications that I think are game
[0:46] changers if you're working with AI. The
[0:48] first is the professional agentic AI
[0:50] certification. This one proves you can
[0:53] actually design and deploy advanced
[0:54] multi- aent systems. And the second is
[0:57] the professional generative AI LLM
[0:59] certification focused on fine-tuning and
[1:02] optimizing large language models for
[1:04] realworld use. These certifications
[1:07] aren't just a piece of paper. They're
[1:08] backed by NVIDIA and actually provide
[1:11] realworld value. Whether you're a
[1:13] student, developer, are already working
[1:15] in the field, NVIDIA certifications are
[1:17] a great way to validate your skills and
[1:19] stand out in a crowded job market. And
[1:21] here's the great thing. You can get 20%
[1:23] off any certification exam with the code
[1:25] tech with Tim 20. I'll leave the link in
[1:28] the description so you can sign up and
[1:29] start leveling up your AI career. So,
[1:31] big thank you to Nvidia for sponsoring
[1:33] this video. Now, let's get into it and
[1:35] start by understanding the core blocks
[1:37] of any AI agent. Now, think of this like
[1:40] your mental checklist when you're
[1:41] planning your agent architecture. Going
[1:44] to go through all of the things that are
[1:45] probably going to make up every AI
[1:47] agent. Now, first, every agent needs an
[1:49] LLM backbone. LLM stands for large
[1:52] language model and this is the brain of
[1:54] your agent that handles understanding
[1:56] language and generating responses. Now
[1:59] you've probably heard of some popular
[2:00] options like OpenAI's GPT4 which powers
[2:03] chat GPT or Entropics Claude. There are
[2:06] also many open- source options like
[2:08] models that come from Olama that you can
[2:10] run locally on your own computer. Now
[2:12] think of the LLM as the reasoning engine
[2:15] that powers everything else. It's what
[2:17] enables your agent to understand tasks,
[2:19] make decisions, and communicate in
[2:21] natural language. So, that's the first
[2:23] thing that you need for any AI agent and
[2:25] LLM, which acts as the backbone. Now,
[2:28] second, you need prompt templates and a
[2:30] reasoning strategy. Prompt templates are
[2:33] pre-esigned text structures that help
[2:34] guide your LLM's responses. Think of
[2:37] these as the questions or instructions
[2:39] that you give to your AI to get useful
[2:41] answers. Now, a good prompt template
[2:43] clearly explains the task, provides
[2:46] context, and specifies the format that
[2:48] you want for the particular response.
[2:50] Now, as for reasoning strategies,
[2:52] there's several popular approaches, and
[2:53] we'll get into them in more detail later
[2:55] in the video. First though, we have
[2:57] react, which stands for reasoning and
[2:59] then action or act. It's where the agent
[3:02] thinks through a problem step by step
[3:03] before taking action. Imagine it like a
[3:06] person thinking, hm, I need to find the
[3:08] weather. First, I'll go access the
[3:09] weather API. Then, I'll look up the
[3:11] user's location, and so on and so forth.
[3:14] Next, we have plan and execute. This is
[3:17] similar, but it separates the planning
[3:19] phase from the execution phase. Lastly,
[3:21] we have reflection, and this is a newer
[3:23] approach that encourages the agent to
[3:25] reflect on its previous actions to
[3:27] improve its future performance. Now,
[3:29] after prompts and reasoning, the third
[3:31] thing that our agent needs is tools and
[3:33] actions that it can use to interact with
[3:36] the world. Now, without tools, your
[3:37] agent is just a chatbot. It can talk,
[3:40] but it can't really do anything. Tools
[3:42] give your agent the ability to take
[3:44] actions beyond just generating text.
[3:46] This can include web access so it can
[3:48] search for information online, file
[3:50] operations like reading or writing
[3:52] files, code execution like running
[3:54] Python code or doing calculations, and
[3:57] API calls like connecting to services
[3:59] like Google Cloud or Slack. Now, think
[4:01] of tools as like the hands of your
[4:03] agent. They let it reach out and
[4:05] actually do things in the digital world.
[4:08] Now, after tools, every agent needs
[4:10] memory and state management. Without
[4:12] memory, your agent would be like a
[4:14] goldfish, forgetting everything as soon
[4:16] as the conversation moves on. Now,
[4:18] memory systems store information from
[4:20] past interactions so your agent can
[4:23] maintain context over time. Now, the
[4:25] simplest form of this is something like
[4:27] buffer memory, which just keeps a record
[4:29] of the recent conversation history. More
[4:31] advanced systems use vector search
[4:33] capabilities to store and retrieve
[4:35] relevant information from a larger
[4:37] knowledge base. And some agents use JSON
[4:40] to store and keep track of structured
[4:42] data like user preferences or a task
[4:44] status. Now that's memory. And finally,
[4:47] at the heart of every AI agent is the
[4:49] control loop. This is the
[4:51] decision-making processes that determine
[4:53] what the agent does next. The control
[4:55] loop continuously cycles through
[4:57] observing the current state, deciding
[5:00] what action to take based on goals and
[5:02] available tools, executing that action,
[5:04] observing the result, and then repeating
[5:06] the process. It's like a thought process
[5:09] of your agent. Now, these are the five
[5:11] components, the backbone, prompt
[5:14] templates, reasoning strategies, tools,
[5:16] and actions, memory, and state
[5:18] management, and finally, the control
[5:20] loop that form the foundation of any AI
[5:23] agent. And by understanding each piece,
[5:25] you'll be better equipped to choose the
[5:26] right frameworks and design patterns for
[5:29] your specific project. So now what I
[5:31] want to do is dive into actual Python
[5:33] frameworks that make building these
[5:35] agents much easier. So let's get into
[5:37] them. All right, so now that we
[5:39] understand the core building blocks,
[5:41] let's break down the most popular Python
[5:43] frameworks for building AI agents. And
[5:45] I've actually used all of these, so I
[5:46] can really speak to them well. Now,
[5:48] first up is Langchain, a modular Python
[5:51] framework that's become the go-to for
[5:53] building LLM applications with tools,
[5:56] memory, chains, and agents. You should
[5:58] consider Langchain when you want full
[6:00] programmatic control. When you're
[6:02] building agents that need to call APIs,
[6:05] perform reasoning tasks, or maintain
[6:07] memory, and when you're comfortable
[6:08] working with Python logic to connect
[6:10] everything together. It's extremely
[6:12] flexible, but there is a little bit of a
[6:14] learning curve, and you do need to know
[6:16] some Python. Now, next on my list is
[6:18] Langraph. This is essentially a stateful
[6:21] graph-based framework built on top of
[6:23] Langchain. Think of it as a structured
[6:25] way to model your agent workflows,
[6:27] giving you a lot more control. Now, you
[6:29] should use Langraphph when you want
[6:30] precise control over how your agent
[6:33] moves through tasks or states when
[6:35] you're building complex multi-step or
[6:37] multi- aent workflows or when you need
[6:39] asynchronous or branching logic like
[6:42] retry mechanics or conditional paths.
[6:44] Now, it's great for more complex agent
[6:46] architectures that need clear state
[6:48] management, but it is a little bit
[6:50] overkill for a basic agent. So, if you
[6:52] want to go with something basic, go with
[6:53] Lang Chain. If you want much more
[6:55] control and something that has a lot of
[6:57] different paths it needs to follow, then
[6:59] go with Langraph. And of course, I have
[7:01] tutorials on both of these on the
[7:02] channel. Now, for those of you that
[7:04] prefer a more visual approach, there is
[7:06] another tool called Langflow. This is
[7:08] pretty much a visual lang chain that
[7:10] lets you drag and drop components to
[7:12] build agents and workflows with minimal
[7:15] coding. Now, this is perfect when you
[7:16] want to prototype something quickly
[7:18] without knowing lang chain or lang graph
[7:20] and when you prefer a visual nodebased
[7:23] interface. It's also great when you're
[7:24] experimenting with different chain
[7:26] configurations and you want to iterate
[7:28] quickly without writing a bunch of code.
[7:30] Now, this is an excellent tool for
[7:31] beginners or for quick proof of concept
[7:33] and of course I have tutorials on this
[7:35] on the channel. Now, when it comes to
[7:37] connecting your agents with data, Llama
[7:39] Index is another standout framework.
[7:41] It's designed specifically to connect
[7:43] external data sources like PDFs,
[7:46] websites, and databases to LLM with
[7:48] powerful indexing, retrieval, and query
[7:51] routing capabilities. So you should
[7:53] consider Llama Index when your agent
[7:55] needs to retrieve context from private
[7:57] data, when you want to build a rag
[7:59] retrieval augmented generation system,
[8:02] or when you need to structure fine-tuned
[8:04] access to files, APIs, or databases.
[8:07] Now, it's the go-to solution for
[8:09] datacentric AI agent applications. So if
[8:12] you're using a lot of data, then check
[8:13] out Llama Index. Now, for more complex
[8:16] scenarios involving multiple agents, you
[8:18] can check out something like Crew AI.
[8:20] Now, this offers a teamwork framework
[8:22] where you can define different roles,
[8:24] assign tasks, and enable collaboration.
[8:27] This is ideal when your use case
[8:28] requires multiple agents that are
[8:30] working together, and when you want
[8:32] agents to follow structured roles and
[8:34] task flows or when you're building
[8:36] simulations of team workflows. Now, this
[8:38] is particularly strong for multi-roll
[8:40] processes like writing, coding, and
[8:43] research projects where different
[8:44] specialized agents need to coordinate
[8:46] together. Now, of course, there are a
[8:48] lot of other tools and frameworks that
[8:49] you can use, but these are the ones that
[8:51] I'm familiar with and that really are
[8:53] the most popular and definitely are
[8:55] going to get you where you need to go
[8:56] when it comes to building advanced AI
[8:58] agents. Now, beyond these frameworks,
[9:00] there are several additional tools that
[9:02] are worth mentioning that will help you
[9:04] when you're writing Python code. Now,
[9:06] number one is Streamlin. This offers a
[9:08] fast way to build web interfaces for
[9:10] your agents. It's extremely simple to
[9:12] use and it's my go-to for user
[9:14] interfaces for AI applications. Next, we
[9:17] have data stacks and Chroma DB. These
[9:20] both provide vector database solutions
[9:22] for storing and retrieving embeddings,
[9:24] and they're good for building in rack.
[9:26] And of course, we have libraries like
[9:27] pandas, for example, which are essential
[9:29] for data manipulation and analysis
[9:31] within your agent workflow. So, consider
[9:34] picking up some of these tools and
[9:35] learning some additional Python modules
[9:37] because they go really nicely with the
[9:39] frameworks I mentioned before. Anyways,
[9:41] let's now explore some common design
[9:43] patterns for AI agents with practical
[9:46] examples to help you understand and how
[9:48] to use each one. Now, first let's talk
[9:51] about the react pattern which stands for
[9:53] reasoning and then action. Now, this
[9:55] originated in academic research and has
[9:57] become the standard approach for tooling
[10:00] agents. In React, your agent first
[10:03] thinks through what it knows and what it
[10:04] needs to find out. It then selects an
[10:07] appropriate action, observes the result,
[10:09] and repeats that for what it needs to
[10:11] do. So, for example, if it was asked to
[10:13] find the population of Tokyo and compare
[10:15] it to New York, a React agent would
[10:17] first reason, I need the population data
[10:20] for two cities. It would then decide to
[10:22] search for Tokyo's population, observe
[10:24] the result, search for New York's
[10:26] population, observe the result, and then
[10:28] finally compare at the numbers. Now,
[10:30] this pattern excels when your agent
[10:32] needs to use tools strategically and
[10:34] explore information in a methodical way.
[10:37] Next, we have the plan and execute
[10:39] pattern. Now, this takes a more
[10:41] structured approach by dividing work
[10:43] between two specialized components.
[10:46] First, you have a planner agent. This
[10:48] develops a comprehensive step-by-step
[10:50] plan to achieve a particular goal. Then
[10:52] you have an executor agent which
[10:54] meticulously follows each step handling
[10:57] any complications that arrive during the
[10:59] implementation. Now think of this like
[11:01] an architect drawing a blueprint before
[11:03] the construction begins. So this pattern
[11:05] shines for complex tasks where mistakes
[11:08] will be costly. For example, if you're
[11:10] writing complex Python script with
[11:12] multiple API integrations, the planner
[11:15] might first outline all of the necessary
[11:16] imports, function definitions, and API
[11:19] calls, well, the executor would write
[11:21] all of the actual code following this
[11:23] blueprint. Now, this separation of
[11:25] concerns leads to more reliable outcomes
[11:27] for sophisticated tasks, and it's
[11:29] definitely something worth considering.
[11:31] Next, we have the multi- aent
[11:33] collaboration. Now, this expands on
[11:35] these foundations by creating teams of
[11:37] specialized agents that work together on
[11:39] complex problems. So, rather than having
[11:41] one agent handle everything, you assign
[11:44] specific roles based on different
[11:46] expertise. For instance, a coding
[11:48] project might involve a project manager
[11:50] agent that defines requirements, a
[11:52] solutions architect that designs the
[11:54] overall structure, and then multiple
[11:56] developer agents that write the code,
[11:58] and maybe a QA agent that tests this for
[12:01] bugs. Now these agents communicate with
[12:03] each other passing information and
[12:05] results between them as the project
[12:07] progresses. Now this approach mimics
[12:09] human team dynamics and works
[12:11] exceptionally well for projects
[12:12] requiring diverse skills and
[12:14] perspectives. Can be hard to set up but
[12:16] when done well it works very well. Next
[12:19] we have retrieval augmented generation
[12:21] or rag. Now this has become an essential
[12:24] pattern for knowledge inensive
[12:26] applications. Now in rag before your
[12:28] agent generates a response it first
[12:30] searches a knowledge base could be
[12:32] something like documents websites maybe
[12:34] a database for relevant information.
[12:37] This is to inform its answer. So for
[12:39] example if you're building a customer
[12:41] support agent the rag pattern would
[12:43] enable it to search through product
[12:44] documentation previous support tickets
[12:47] maybe information about the company
[12:49] before answering a customer's question.
[12:51] Now, this dramatically improves the
[12:53] accuracy by grounding the responses in
[12:55] factual information rather than relying
[12:58] solely on the LLM's internal knowledge
[13:00] which could be out ofd or just not
[13:02] relevant to the particular problem. Now,
[13:04] rag is very valuable when you're working
[13:06] with domain specific information,
[13:09] proprietary data, or rapidly changing
[13:11] knowledge that might not be in the
[13:13] existing LLM training data. So, how do
[13:16] you actually choose your stack and your
[13:18] architecture? Well, my advice is to
[13:20] start simple. One agent, one clear goal,
[13:23] no complex memory requirements, and as
[13:26] you understand the problem better, you
[13:28] can scale up by adding tools, memory
[13:30] systems, planning capabilities,
[13:32] team-based approaches, etc. Now, in
[13:34] terms of your choice of framework, you
[13:36] should be guided by your priorities. If
[13:38] you need full control, go with Langchain
[13:40] or Langraph. If collaboration between
[13:42] agents is key, then use something like
[13:44] Crew AI. And if you need to demo
[13:46] something quickly, you could use
[13:48] Streamlit and something like Langflow to
[13:50] get something up and running quickly.
[13:51] Lastly, if something like Privacy is a
[13:53] major concern, then definitely consider
[13:55] using some local models with tools like
[13:58] Olama, for example, which allow you to
[14:00] run models locally on your own computer,
[14:02] assuming that you have sufficient
[14:04] hardware. Now, the beauty of this field
[14:06] is that it's evolving rapidly with new
[14:08] tools and patterns every single day. So
[14:10] what matters most is understanding the
[14:12] fundamental building blocks and the
[14:14] trade-offs between different approaches.
[14:16] So start with a clear problem statement.
[14:18] Choose the simplest stack that addresses
[14:20] your needs and iterate from there. All
[14:23] right guys, so that's going to wrap up
[14:24] this video. I know that was a lot of
[14:26] information, but I wanted to provide a
[14:28] high-level kind of structured guide that
[14:30] goes over the key components of AI
[14:32] agents, some important frameworks that
[14:34] you might want to be aware of, and then
[14:36] of course the different design patterns
[14:37] so you have somewhere to start and you
[14:39] understand what's possible in this
[14:41] field. If you enjoyed the video, make
[14:43] sure to leave a like, subscribe to the
[14:45] channel, and I will see you in the next
[14:46] one.
[14:49] [Music]