4 Agentic Frameworks for More Efficient Workflows in n8n

so in my ultimate assistant video we utilize an agentic framework called parent agent so as you can see we have a parent agent right here which is the ultimate assistant that's able to send tasks to its four child agents down here which are different workflows that we built out within naden if you haven't seen that video I'll tag it right up here but how it works is the ultimate assistant could get a query from the human and decide that it needs to send that query to the email agent which looks like this and then the

email agent will be able to use its Tools in Gmail and actually take action from there it responds to the parent agent and then the parent agent is able to take that response back from the child agent and then respond to us in telegram so it's a super cool system it allows us to delegate tasks and also these agents can be activated in any specific order it doesn't always have to be the same but is this framework always the most effective no so today I'm going to be going over four different agentic Frameworks that you

can use in your NN workflows the first one we're going to be talking about is prompt chaining the second one is routing the third one is parallelization and the fourth one is evaluator Optimizer so we're going to break down how they all work what they're good at but make sure you stick around to the end because this one the evaluator Optimizer is the one that's got me most excited so before we get into this first framework if you want to download these four templates for free so you can follow along you can do so by

joining my free school Community you'll come in here click on YouTube resources click on the post associated with this video and then you'll have the workflow right here to download so the link for that's down in the description there's also going to be a link for my paid Community which is if you're looking for a more Hands-On approach to learning nadn we've got a great community of members who are also dedicated to learning nadn sharing resources sharing challenges sharing projects stuff like that we've got a classroom section where we're going over different Deep dive topics

like building agents vect databases apis and HTTP requests and I also just launched a new course where I'm doing the step-by-step tutorials of all the videos that I've shown on YouTube and finally we've got five live calls per week to make sure that you're getting questions answered never getting stuck and also networking with individuals in the space we've also got guest speakers coming in in February which is super exciting so I'd love to see you guys in these calls anyways back to the video here the first one we're going to be talking about is prompt

chaining so as you can see the way this works we have three agents here and what we're doing is we're passing the output of an agent directly as the input into the next agent and so on so forth so here are the main benefits of this type of workflow it's going to lead to improved accuracy and quality because each step focuses on a specific task which will help reduce errors and hallucinations greater control over each step we can refine the system prompt of the outline writer and then we can refine The Prompt of the evaluator

so we can really tweak what's going on and how data is being transferred specialization is going to lead to more effective agents so as you can see in this example we're having one agent write the outline one of them evaluates the outline and makes suggestions and then finally we pass that revised outline to the blog writer who's in charge of actually writing the blog so this is going to lead to a much more cohesive thought through actual blog in the end compared to if we would just fed in all of this system prompt into one

agent and then finally with this type of framework we've got easier debugging and optimization because it's linear we can see where things are going wrong finally it's going to be more scalable and reusable as we're able to plug in different agents wherever we need them okay so what we have to do here is we're just going to enter in a keyword a topic for this um blog so I'm just going to enter in coffee and we'll see that the agents start going to work so the first one is an outline writer um one thing that's

also really cool about this framework and some of the other ones we're going to cover is that because we're splitting up the different tasks we're able to utilize different large language models so as you can see the outline writer we gave 20 flash because it's it's free um it's it's powerful but not super powerful and we just need a brief outline to be written here and then we can pass this on to the next one that uses for mini it's a little more powerful a little more expensive but still not too bad and we want

this more powerful chat model to be doing the evaluating and refining of the outline and then finally for the actual blog writing content we want to use something like claw 3.5 or even deep seek R1 because it's going to be more powerful and it's going to take that revised outline and then structure a really nice blog post for us so that's just part of the specialization not only can we split up the tasks but we can plug and play different chat models where we need to rather than feeding everything through one you know one deep

seek r one blog writer at the very beginning so this one's finishing up here it's about to get pushed into a Google doc where we'll be able to go over there and take a look at the blog that I got for us about coffee so looks like it just finished up here we go detailed blog post based on option one a comprehensive guide to Coffee here's our title um we have a rich history of coffee from Bean to cup we have um different methods we have different coffee varieties we have all this kind of stuff

health benefits and risks um and as you can see this pretty much was a four-page Blog we've got a conclusion at the end anyways let's dive into what's going on here so the concept is passing the output into the input and then taking that output and passing it into the next input so here we have here's the topic to write a blog about which all it got here was the word coffee that's what we typed in the system message is that you an expert outline writer your job is to generate a structured outline for a

blog post with Section titles and key points so here's the first draft at the outline using twoof flash then we pass that into an outline evaluator that's using foral mini we said here's the outline we gave it the outline of course and then the system message is you're an expert blog evaluator your job is to revise this outline and make sure it hits these four criteria which are engaging uction clear section breakdown logical flow and then a conclusion so we told it to only output the revised outline so now we have a new outline over

here and finally we're sending that into a CLA 3.5 blog writer where we gave it the revised outline and just said you're an expert blog writer generate a detailed blog post using this outline with well structured paragraphs and engaging content so that's how this works you can see it will be even more powerful once we hook up you know like some internet search functionality and if we added like an editor at the end before it actually pushed it into the the Google Doc or whatever it is but that's how this framework works but let's move

into agentic framework number two now we're going to talk about the routing framework in this case we have an initial llm call right here to classify incoming emails and based on that classification it's going to Route it up as high priority customer support promotion or finance and billing and as you can see there's different actions that are going to take place we have different agents depending on what type of message comes through so the first agent which is the text classifier here basically just has to decide okay which agent do I need to send this

email off to anyways why would you want to use routing because you're going to have an optimized response handling so as we could see in this case we're able to set up different personas for each of our agents here rather than having one General AI response agent then this can be more scalable and modular it's going to be faster and more efficient and then you can also introduce human escalation for critical issues like we do up here with our high priority agent and finally it's just going to be a better user experience for for your

team and also your customers so I hit test step what we're getting here is an email that I just sent to myself that says hey I need help logging into my account can you help me so this email classifier is going to label this as customer support as soon as we hit play it's going to send it down the customer support Branch right here as you can see we got one new item what's going on in this step is that we're just labeling it in our Gmail as a customer support email and then finally we're

going to fire it off to the customer support agent in this case this one is trained on customer support activities um this is where you could hook up a customer support database if you needed and what it's going to do is it's going to create an email draft for us in reply to the email that we got so let's go take a look at that so here's the email we got hey I need help loging into my account as you can see our agent was able to label it as customer support and then finally it

created this email which was hey Nate thanks for reaching out I'd be happy to assist you with logging into your account please provide me with some more details um about the issue you're experiencing blah blah blah and then this one signs off best regards Kelly because she's the customer support rep okay let's take a look at a different example um we'll pull in the trigger again and this time we're going to be getting a different email so as you can see this one says Nate this is urgent we need your outline tomorrow or you're fired

so hopefully this one gets labeled as high priority it's going to go up here to the high priority Branch once again we're going to label that email as high priority but instead of activating an email draft reply tool this one has access to a telegram tool so what it's going to do is text us immediately and say hey this is the email you got you need to take care of this right away um and obviously the logic you can choose of what you want to happen based on what route it is but let's see we

just got telegram message urgent email from Nate herkelman stating that an outline is needed by tomorrow or there will be serious consequences potential termination so that way it notifies us right away we're able to get into our email manually you know get get caught up on the thread and then respond how we need to and so pretty much the same thing for the other two promotional email will get labeled as promotion we come in here and see that we are able to set a different persona for the promotion agent which is you're in charge of

promotional opportunities your job is to respond to inquiries in a friendly professional manner and use this email to send reply to customer all we sign off as Meredith from ABC Corp so each agent has a different sort of persona that it's able to respond to in finance agent we have we have this agent signing off as as Angela from ABC Corp um anyways what I did here was I hooked them all up to the same chat model and I hooked them all up to the same tool because they're all going to be sending an email

draft here as you can see we're using from AI to determine the subject the message in the thread ID which it's going to pull in from the actual Gmail Trigger or sorry the Gmail trigger is not using fromi we're we're mapping in the Gmail trigger because every time an email comes through it can just look at that um email in order to determine the thread ID for sending out an email but you don't have to connect them up to the same tool I just did it this way because then I only had to create one

tool same thing with the different chat models based on the you know importance of what's going through each route you could switch out the chat models we could have even used a cheaper easier one for the classification if we wanted to but in this case I just hooked them all up to a 40 mini chat model anyways this was just a really simple example of routing you could have 10 different routes you could have just two different routes but the idea is that you're using one agent up front to determine which way to send off

the data moving on to the third framework we've got parallelization what we're going to do here is be using three different agents and then we're going to merge their outputs aggregate them together and then feed them all to a final agent to sort of you know throw it all into one response so what this is going to do is give us faster analysis rather than processing everything linearly so in this case we're going to be sending in some input and then we're going to have one agent analyze the emotion behind it one agent do the

intent behind it and then one agent analyze any bias rather than doing it one by one they're all going to be working simultaneously and then throwing their outputs together so it can decrease the latency there they're going to be specialized which means we could have specialized system prompts like we do here we also could do specialized um large language models again where we could plug in different different models if we wanted to maybe feed through the same prompt use CLA up here open a eye down here and then you know deep seek down here and

then combine them together to make sure we're getting the best thought out answer um comprehensive review and then more scalability as well but how this one's going to work is we're putting in an initial message which is I don't trust the mainstream media anymore they always push a specific agenda and ignore real issues people need to wake up and stop believing everything they see on the news so we're having an emotion agent first of all analyze the emot emotional tone categorize it as positive mutal negative or mixed with a brief explanation the intent agent is

going to analyze the intent behind this text and then finally the bias agent is going to analyze this text for any potential bias so we'll hit this off um we're going to get those three separate analysises um or analysis and then we're going to be sending that into a final agent that's going to basically combine all those outputs and then write a little bit of report based on our input so as you can see right now it's waiting here for um the input from the bias agent once that happens it's going to get AGG created

and now it's being sent into the final agent and then we'll take a look at um the report that we got in our Google doc okay just finished up let's hop over to docs we'll see we got an emotional tone intent and bias analysis report overview is that um the incoming text has strong negative sentiment towards mainstream media yep emotional tone is negative sentiment intent is persuasive goal um the bias analysis has political bias generalization emotional language lack of evidence um it's got recommendations for how we can make this text more neutral Revis message and

then let's just read off the conclusion the analysis highlights a significant level of negativity and bias in the original message directed towards mainstream media by implementing the suggested recommendations the author can promote a more balanced and credible perspective that encourages critical assessment of media consumption blah blah blah so as you can see that's going to be a much better you know comprehensive analysis than if we would have just fed the initial input into an agent and said hey can you analyze this text for emotion intent and bias but now we got that split up merged

together put into the final one for you know a comprehensive review and an output and it's going to turn the the you know data in into Data out process it's going to be a lot more efficient finally the one that gets me the most excited um the evaluator Optimizer framework where we're going to have an evaluator agent decide if what's passing through is good or not if it's good we're fine but if it's not it's going to get optimized and then sent back to the evaluator for more evaluation and this is going to be an

endless loop until the evaluator agent says okay finally it's good enough we'll send it off so if you watch my human in the loop video it's going to be just like that where we were providing feedback and we were the ones basically deciding if it was good to go or not but in this case we have an agent that does that so it's going to be optimizing all your workflows on the back end without you being in the loop so obviously the benefits here are that it's going to ensure high quality outputs it's going to

reduce errors and manual review it's going to be flexible and scalable and then it's going to optimize the ai's performance because it's sort of an iterative approach that um you know focuses on continuous improvement from these AI generated responses so what we're doing here is we have a biography agent what we told this agent to do is um basically write a biography you're an expert biography writer you'll receive information about a person your job is to create an entire profile using the information they give you and I told it you're allowed to be creative from

there we're setting the bio and we're just doing this here so that we can continue to feed this back over and over that way if we have five revisions it'll still get passed every time the most recent version to the agent and also the most recent version when it's approved will get pushed up here to the Google Doc then we have the evaluator agent what we told this agent to do is um evaluate the biography your job is to provide feedback we gave a criteria so make sure that it includes a quote from the person

make sure it's light and humorous and make sure it has no Emojis only need to Output the feedback if the biography is finished and all criteria are met then all you need to Output is finished so then we have a check to say okay does the output from the evaluator agent say finished or is it feedback if it's feedback it's going to go to the optimizer agent and continue on this Loop until it says finished once it finally says finished as you can see we set json. output which is the output from the evaluator agent

equals finished when that happens it'll go up here and then we'll see it in our Google doc but then what we have in the actual Optimizer agent is we're giving it the biography and this is where we're referencing the set field where we earlier right here where we set the bio this way the optimizer agent's always getting the most updated version of the bio and then we're also going to get the feedback so this is going to be the output from the evaluator agent because if it does go down this path the evaluator agent means

that it output feedback rather than saying finished so it's getting feedback it's getting the biography and then we're saying you're an expert reviser your job is to take the biography and optimize it based on the feedback so it gets all it needs in the user message and then it outputs us a better optimized version of that biography okay so let's do an example real quick um if you remember in the biography agent well all we have to do is give it a you know some information about a person to write a biography on so I'm

going to come in here and I'm just going to say gym 42 um lives by the ocean okay so that's all we're going to put in we'll see that it's writing a brief biography right now and then we're going to see it get evaluated we're going to see if it you know met those criteria if it doesn't it's going to get sent to the optimizer agent the optimizer agent is going to get um basically the criteria it needs to hit as well as the original biography so here's the evaluator agent look at that it decides

that it wasn't good enough now it's being sent to the optimizer agent who is going to optimize the bio send it back and then hopefully on the second run it'll go up and get published in the docs if it's not good enough yet then it will come back to the agent and it will optimize it once again but I think that this agent will do a good job there we go we can see it just got pushed up into the dock so let's take a look at our Google doc here's a biography for Jim Thompson

he lives in California he's 43 um ocean Enthusiast passion Adventure a profound respect for nature it talks about his early life and obviously he's making all this up talks about his education talks about his career talks about his personal life here we have a quote from Jim which is I swear the fish are just as curious about me as I am about them we've even got another quote um a few dad jokes along the way why did the fish blush because it saw the ocean's bottom so not sure I completely get that one oh no

I get that one um anyways then Hobbies philosophy Legacy and a conclusion so this is you know a pretty optimized blog post it meets all the criteria that we had put into our agents as far as you know this is what you need to evaluate for it's very light there's no Emojis thre some jokes in there and then it has some quotes from Jim as well so as you can see all we put in was Jim 43 livz by the ocean and we got a whole basically a story written about this guy and once again

just like all of these Frameworks pretty much you have the flexibility here to change out your model wherever you want so let's say we don't really mind up front we could use something really cheap and quick and then maybe for the actual Optimizer agent we want to plug in something a little more um you know with reasoning aspect like deeps car1 potentially anyways that's all I've got for you guys today hope this one was helpful hope this one you know sparked some ideas for next time you're going into Ed end to build an agentic workflow

maybe looking at I could actually have structured my workflow in this framework and it would have been a little more efficient than the current way I'm doing it like I said these four templates will be in the free school Community if you want to download them and just play around with them to understand what's going on understand you know when to use each framework stuff like that anyways as always I really really appreciate you guys making it to the end of this one if you learned something new if it helps you out please give it

a like it definitely helps me out and um yeah um I'll see you guys in the next video