🛠️ Building Iterative AI Agents with LangGraph and Gemini
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Hey, everyone. Welcome back to the Deep Dive. You know how AI is getting, well, smarter, tackling problems that used to seem just impossible. Yeah, it's really moving fast. And a big part of that, it seems, is moving beyond just simple answers. AI is starting to think more like us, breaking things down, refining work, even like correcting its own mistakes. Exactly. It's the shift towards AI.

AI agents that can deliberate, iterate, you know, improve their output within a single go. It's really changing the game. And that's exactly what we're diving to today using this fantastic new resource. It's a brand new tutorial from the AI Unraveled Builders Toolkit. Oh, yeah, I saw that drop. Yeah. And this toolkit, it's created by Etienne Newman. He's a senior engineer up in Canada. And I love this. Do you feel a passionate soccer dad? Huh? That's

That's great. Brings a nice grounded perspective to all this high-level tech, doesn't it? It really does. So, okay, our mission for this deep dive is to unpack this specific tutorial for you. We're going to walk you through the key ideas, the concepts, and the practical steps of building one of these iterative AI agents.

Using some really powerful tools. Right, exactly. We'll be looking at libraries like Landgraf and the smarts behind models like Google's Gemini. The goal is pretty simple, really. Help you understand how these agents work, why they're needed, and get you feeling ready to maybe build one yourself. Taking it from theory to...

okay, I can actually do this. Totally. So if you're interested in AI that can, well, actually think and refine its work, you are definitely in the right place. Do us a quick favor. Like this deep dive, subscribe to the show. It's honestly the best way to catch all our future explorations and these kinds of hands-on guides. And just to jump in quickly, everything we're talking about conceptually today, it's laid out step-by-step, ready for you to build in the AI Unraveled Builder's Toolkit. Okay.

Oh, good point. Yeah, it's got practical tutorials, PDF, audio, video formats, so whatever works best for you. And you can find the links to grab the AI Unraveled Builders Toolkit right in our show notes over at djamgate.com.

It really helps bridge that gap. Between understanding and actually doing. Yeah. So over the next little while, we'll cover why we need this iterative approach, break down the core building blocks Landgraf gives you, show you the specific design of the agent in a TN's tutorial, and crucially explain how you can actually watch it.

think step by step. Okay, let's kick things off with that big question. Why? Why do we even need AI agents that iterate? What's wrong with the old way? Yeah, good question. I mean, why not just ask the LLM once and be done? Yeah. The source material really emphasizes that modern AI, especially with these super powerful LLMs, it often needs to do more than just a single pass. Right. Think about complex tasks.

real problem-solving or generating something creative like writing a detailed report or debugging tricky code maybe planning a complicated trip you don't just do it in one shot right you gather info maybe draft something you review it you spot flaws maybe you need to go back and research more then you refine it that loop that's how humans get good results right we iterate we loop back and improve

A single call to an LLM often just can't capture that depth or that refinement process. Exactly. And that's where this idea of the iterative AI agent comes in. These are systems specifically designed to tackle a task through multiple steps, looping back, reevaluating, refining their own work based on, you know, internal checks or feedback. Okay, so the goal is building agents that can think in loops. And the library that's really geared for this, building on Langchain, is Langraph.

You mentioned its superpowers, creating graphs with cycles.

That's the absolute key differentiator. Traditional Lang chain chains are often linear, like A, then B, then C. Lang graph lets you build workflows that can actually circle back, go from C back to B, for instance. And that cyclical structure, that's what enables these agents to reevaluate, to self-correct, to do that kind of multi-step reasoning or deliberation we're talking about. It's like giving the AI workflow a memory and the ability to change its mind based on what it learned.

That's a great way to put it. And for the actual brainpower inside the structure, the part that understands the query, analyzes stuff, generates the text, the tutorial uses Google's Gemini models, specifically Gemini 1.5 Flash in this case.

you access that power through an API call. Gotcha. So you've got Landgraf providing the sturdy framework, the loops, the control flow, and then you plug in Gemini's advanced smarts to actually do the thinking and writing within that flow. That's the core combo here. It's a really potent combination, structure meets intelligence. Okay. I think I'm getting the why now and the main tools. Let's dig into the actual building blocks from Landgraf that make this possible.

The tutorial lays these out pretty clearly. - Yeah, LyGraph structures things as a graph, right? And there are a few key components you really need to get your head around. - First up is the state. The tutorial calls it the agent's working memory.

What does that mean in practice? Okay, the state is all it's probably the most crucial piece for these stateful agents. Think of it as a shared data container could be a simple Python dictionary, maybe a data class, something like that. And this container, it sticks around, it persists, and it accumulates information as the agent runs. So every step, every node can see it and add to it. Exactly. Every node gets the current state as input, does its job,

And can return updates to that state. And that's how it remembers things across steps, even across loops. Precisely. And this is where the magic of iteration really happens. The state holds the original request, sure, but also any intermediate results, context it gathered, and critically, feedback from previous steps. Ah, okay.

So when the agent loops back, maybe because a validation step failed, it's now working with a richer state than the first time around. The tutorial shows this perfectly. Validation feedback gets added to the state. Right. And the agent uses that feedback in the next loop to try and make a better response. Wow. Okay. So the state is like the engine driving the self-improvement within a single run. It lets the agent learn from its own process right there and then. Couldn't have said it better myself. It is the engine for that. Okay. Next up.

These are the actions, right? What the agent actually does. Yep. Nodes are the individual work units. In this tutorial, they're mostly Python functions, or you might see them as LCL runnables, basically just modular chunks of logic. Right. Each node takes the current state, performs a specific task, maybe call an LLM to analyze something, maybe simulate a search, maybe generate the actual response, and then it spits out updates for the state.

So you build the agents capabilities by defining these separate modular actions. Seems cleaner. Much cleaner. Each node does one thing well, makes it easier to build and debug. Right. State holds the memory. Nodes do the work. What connects them? The edges, right? How do they handle the iteration part? Edges strike the flow. Yeah. You've got your basic normal edges. Just go from node A straight to node B. Simple. But the real power for making these dynamic looping agents comes from conditional edges. Okay. Tell me more.

How do they enable the loops? A conditional edge isn't just a fixed path. It's like a fork in the road. It has a little function associated with it that looks at the current state, what the agent knows right now, and decides which node to go to next. Ah, okay. So the edge itself contains logic. Exactly. It examines the state and returns, say, a string naming the next node.

This is how the agent decides, okay, based on that validation feedback in the state, do I need to loop back to the analyzer node or is the work good enough to go to the ND node? So the conditional edge is where the agent's branching logic lives based on everything that's learned and stored in its state. You got it. That's how you implement that critical decision making, the ability to say, is this good? No. Okay. Go back and try again.

The tutorial also mentions setting an entry point where it all starts, and that special nd node, which just signifies "we're done". So putting it all together.

The state provides the memory and context, the nodes perform the actions, and these smart conditional edges use the state to route the flow, potentially creating those cycles. That's how Landgraf lets you build these complex iterative workflows. That's the core loop, yeah. The agent can keep working, refining its output, until some condition in the state tells a conditional edge, okay, perfect.

Proceed to end. And the thing that orchestrates all this that you use to actually build the graph, that's the state graph class. Yep. State graph is how you assemble it. You add your nodes, define your edges, normal and conditional, set the entry point, and then you dot compile it into an actual runnable application.

Man, understanding these pieces, state, nodes, those conditional edges especially, and state graph feels absolutely fundamental if you want to build AI that does more than just simple straight line tasks. It really is. Mastering these isn't just about building cool agents. It's key for anyone serious about understanding modern AI workflows and frankly, boosting their career in this space. And speaking of careers and mastering fundamentals.

If you are looking for structured ways to really nail these concepts, maybe even get certified.

Etienne Newman, the creator of the toolkit we're using. Yeah. He also has a great series of A.I. certification prep books. Oh, right. I've seen those. They cover things like the Azure A.I. Engineer Associate, the Google Cloud Generative A.I. Leader Cert. Yep. AWS Certified A.I. Practitioner, Azure A.I. Fundamentals, Google Machine Learning Certification.

A really solid lineup. So they can really help build that foundational knowledge you need for the exams and for real world projects. Exactly.

And you can find links to all of Etienne's AI certification books right alongside the AI Unraveled Builders Toolkit over at djamgat.com. We put all the links in the show notes for you. Easy access. Definitely worth checking out if you're looking to formalize your skills. Okay, so we've got the Y and the line graph building blocks. Let's switch gears now and look at the specific agent design in this tutorial. How does it actually use state, nodes, and edges? Right, so the tutorial guides you through building what it calls an intelligent query handler.

The basic idea is to create an agent that takes a question or a request from a user and processes it intelligently, not just spitting back one answer, but potentially going through this multi-step refinement process we've been talking about. Okay, so what can this query handler actually do? What's built into its design? Well, according to the source material, it's designed to first really understand the query, then analyze what's actually needed. Based on that, it might decide to do some research, then it generates a response.

But here's the key part. It then validates that response internally. Ah, the self-correction loop. Exactly. If that validation step says, not quite right, the agent is designed to iterate loop back, use that feedback, and

and try to refine its output, just like, you know, a human expert might review their own draft and improve it. That built-in validation and refinement, that's the core of the intelligent part then. Okay, walk us through the flow. What are the nodes and decisions? Sure thing. It starts with the user's input query, naturally. One, first it hits the router node.

Its job is basically initial analysis, maybe setting some initial context in the state. Okay, firstly. Two, then it goes to the analyzer node. This is crucial. It looks at the query, looks at the state, and makes a key decision. Do I need more info? Do I need to research? Or can I answer this directly? Makes sense. Respond. Yeah. Three, that decision leads to the first conditional branch controlled by a conditional edge.

If the analyzer said research, off it goes to the researcher node. If it said respond, it bypasses research. Got it. Branching based on need. Four. The researcher node, if it gets called, simulates fetching exercises.

extra info. Think like a quick web search. And critically, it adds whatever it found to the context field in the state. Building up that memory. Right. Now, whether it did research or not, the flow comes together at the responder node. This node takes everything in the state, original query, analysis, any research, and generates the actual answer. Okay, the main output generator. Six, but it's not done. That response goes straight to the validator node. This is that self-correction step. The

The validator looks at the response, compares it to the query in context, and decides, is this good? Complete. Or does it need work? Needs improvement. The internal critic. Pretty much. Seven. And that leads to the second, maybe most important, conditional branch. Another conditional edge checks the validator's output. If it's complete, great. We're done.

Or if the agent has looped too many times, hits that max iteration safety limit in the state. Right, got to prevent infinite loops. Then it also goes to the end node, returning the best response it has, 8.

But T in here is the loop. If the validator said needs improvement and we haven't had the iteration limit, that conditional edge sends the flow back to the analyzer node. Whatever amount we go again. Exactly. And this time, the analyzer gets a state that includes the previous bad response and the validator's feedback in the context. So it has more info to guide the next attempt. Wow. That flow, especially looping back from the validator,

It really does mirror how a person might tackle a tough question. Think, gather info, answer, check work, revise. It's a very human-like problem-solving process just mapped onto this graph structure. It's quite elegant actually. And the agent state, the specific memory structure for this handler

What are the absolute key things it needs to track for that loop to function? Yeah, the state is the glue holding it all together. For this specific query handler, it needs to track things like the query itself, the original input,

Stays the same. Okay. The context, this is vital. It's like a running log. The router adds notes, researcher adds findings, and importantly, the validator adds its feedback here. This growing context is how the agent learns from one leap to the next. The iterative memory. Then things like analysis, what the analyzer decided, response, what's the current answer, maybe next action to help guide branching, and definitely iteration just to counter, one, two, three. To track the loops. And max iterations, that safety valve we mentioned, to stop it running forever if it gets stuck.

The state really is that connective tissue, isn't it? Holding all the evolving pieces, making the whole intelligent iteration thing possible. Absolutely. It's the agent's internal workspace, enabling that statefulness and learning within a single execution. Okay. Understanding the design is one thing, but how do you actually turn this into code that runs?

And maybe more importantly, how do you peek inside and see that iteration happening? Right, the implementation. The tutorial obviously dives deep into the Python code.

You'll need your Python setup and then install the main libraries. Langraph itself, Langchain, Google Genie for talking to Gemini, and Python Dotenv is usually recommended for handling API keys safely. Ah, yes, API keys. The source material definitely stresses being careful with those. Oh, absolutely. It's a non-negotiable best practice highlighted in the tutorial. Never, ever hard code API keys in your scripts.

or check them into Git. Use environment variables, load them from a .env file, and make sure that .env file is listed in your .gitignore. Good reminder, leaped keys can get very expensive very fast. You bet. So, okay, you load your keys securely, then you initialize the connection to the LLM. Using the laying chain classes, right? Yeah, like chat Google generative AI, telling it which model you want, like Gemini 1.5 Flash Latest. And the tutorial points out something interesting here, too.

You could potentially use different LLM instances with different settings for different nodes. Oh, like different temperature settings. Why do that? Exactly. Temperature controls the randomness or creativity of the output. So maybe for the responder node where you want a potentially creative answer, you might use a higher temperature like 0.7. Makes it less predictable. Right. But for the analyzer node, which needs to make a clear decision...

research or respond, or especially for the validator node that needs to give a reliable, complete, or needs improvement judgment. You want consistency there. Precisely. So for those, you might use a much lower temperature, like 0.1, to make the output more focused and deterministic. It's a neat way to fine-tune the LLM's behavior for each specific job in the workflow. That's a really clever technique for controlling the agent.

Okay, so you set up the state structure like a data class. You write your Python functions for each node. You write the functions for the conditional edges. Correct. The conditional edge functions just need to look at the state and return the name of the next node. And then you use that state graph class to wire it all up. Add nodes, add edges, set the entry point, define the conditional logic, and then .compile. Yep, .compile turns your graph definition into a runnable application object.

Now, for folks following along or who really want the actual code to build this themselves, step by step.

Where do they find that? Ah, well, that's exactly where the AI Unraveled Builder's Toolkit comes in. We're sketching out the architecture and the logic here, right? But the toolkit gives you the full, detailed, step-by-step coding guide. The practical implementation. Exactly. It has the instructions, the actual Python code snippets, all laid out in those PDF audio and video tutorials. You can literally follow along and build exactly what we've been discussing.

So this deep dive gives you the blueprint. The toolkit gives you the tools and instructions to actually build the house. That's a good analogy. It makes building these more complex agents way more approachable. And remember, you can grab the toolkit at djamgettech.com. Links in the show notes. Okay, great. So imagine you've built it, you've compiled it, it's running.

How do you actually see the iteration? Just getting the final answer doesn't tell you the story of how it got there. No, it doesn't. And this is where one of Landgraf's most valuable features shines, its streaming capabilities. Honestly, for these complex cyclical agents, being able to stream the internal steps is just paramount.

Paramount for understanding, for debugging, for trusting the agent. Right. Without it, it's a black box. Pretty much. But when you run your compiled graph, you can ask it to stream the output. The tutorial mentions two useful stream mode options, values and updates.

What's the difference there? Values is really verbose. It gives you the entire state dictionary after every single step after each node runs. Great for seeing the full picture. Updates is more concise. It just shows you what changed in the state after that node ran. What updates did it return? Both are super useful for watching the agent's journey unfold.

It's like watching its thought process live. Exactly. And when you're watching that stream, what should you look for? What confirms it's actually iterating and refining? Yeah. What are the key signals? You want to watch a few things closely.

Look for the iteration count in the state. See it tick up from one to two, especially after it seems to loop back from the validator. That's your proof of iteration. Watch the context field. See how it grows. Initial notes, then maybe research findings get added. And critically, watch for the validator's feedback appearing there. That shows it's learning and carrying info forward. Right. The accumulating knowledge. Keep an eye on the analysis and response fields.

You might see the response change significantly between iteration 1 and iteration 2, showing the refinement based on the feedback now in the context.

Also track things like next action to see the decisions being made. And honestly, the simple print statements inside each node function in the tutorial's code are also super helpful just for seeing, okay, now the router is running, now the validator is running. Ah, yeah, good old print debugging. The tutorial even has conceptual snapshots of the state. Yeah, which really helps visualize how the state evolves through the loops.

Seeing that stream just makes the whole abstract graph and state idea really concrete. It brings the agent's process to life. It sounds crucial for transparency, for actually understanding what your agent is doing and why. Absolutely essential for building anything robust, I'd say. Well, this has been a really fascinating look under the hood of building these more intelligent, iterative AI agents. Yeah, it really highlights how combining a solid structural framework like LandGraph

with powerful models like Gemini lets us move beyond simple request response. We can build systems that handle complexity, that deliberate, that self-correct much more like humans do. Landgraf gives you that essential structure.

The stateful memory, the modular actions and nodes, that crucial cyclical control with conditional edges. While Gemini provides the advanced intelligence needed within that structure to do the analyzing, researching, and responding. And the patterns you learn here, building this query handler, they're really versatile. This iterative template state, nodes, validation, loop. You can adapt that for so many other tasks that need refinement.

Think self-correcting code generation. Oh, yeah. Or iteratively improving summaries based on feedback or multi-step planning that adapts on the fly. It's a foundational pattern. It really feels like a big step towards AI that's not just more capable, but also more understandable, more controllable because you have this explicit structure and visibility thanks to frameworks like LineGraph. That control and transparency, increasingly vital as AI tackles more critical jobs.

Definitely. Okay. Before we wrap up, just one final reminder for everyone listening. This whole deep dive, the specific agent design, the implementation details we talked about, it comes straight from Etienne Newman's AI Unraveled Builders Toolkit. The source material for today. Exactly. So if this sparked your interest, if you want to actually get your hands dirty, write the code, build this agent yourself. Mm-hmm.

The toolkit is where you need to head. And it's got those different formats, right? PDF, audio, video to suit how you learn best.

That's right. Just head over to DJMgateTech.com. The link is right there in the show notes. Grab the toolkit and you can start building your own iterative AI agents today. And while you're on DJMgate.com. Oh, yeah. Don't forget Etienne's AI certification books, too. If you're serious about mastering these concepts for your career, boosting your skills, getting certified, they're a fantastic resource covering those foundational topics. Also linked in the show notes. Excellent resources for structured learning and AI. Definitely check them out. Okay.

Okay, so finally, let's leave you with a thought to chew on. Something that builds on the potential extensions mentioned in the tutorial itself. This iterative agent we discussed, it's really just a starting point. Mm-hmm, just scratching the surface. Imagine plugging real tools into that research node, like actual live web search, or giving the agent a longer-term memory so it can handle real conversations over multiple turns.

Or maybe even adding a step for human-in-the-loop feedback, where you could guide its next iteration. Yeah, those possibilities really show the power that this iterative approach unlocks. You can create systems that become more and more sophisticated, more reliable, even collaborative. So here's the question for you. Think about a complex, challenging problem you face.

What task could you design an iterative agent to solve using this pattern of state, nodes, and that crucial self-correction loop? Where could that structured iteration make a real difference for you? Something to think about. Definitely. Well, thanks for joining us for this deep dive. Yeah, great discussion. We'll see you next time on the deep dive.