Why Agent Frameworks Will Fail (and what to use instead)

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You probably don't need an agent framework to solve your automation problem. In this video, I'll cover my approach.
👋🏻 About Me
Hi there! I'm Dave, an AI Engineer and the founder of Datalumina. On this channel, I share practical coding tutorials to help you become better at building intelligent systems. If you're interested in that, consider subscribing!

[@alvandal]

Great video, I am not big on KZbin, but this is the first time I see someone really understanding the current state of the tech.

[@istvandarvas3372]

I mostly agree with everything. But there are two kinds of pipelines. The first one is when you have a finite amount of transformations, and the second one is when you don't know all the transformations in advance and need to delegate decision-making (in this case, you need an agentic approach). However, every pipeline can be represented as a finite transformation when you know it. for example classification. and t that is the key. So, if your pipeline is research-like, then you can't know it in advance; in other cases, you can.

[@matterhart]

I like this distinction. I build UI heavy pipelines with a lot of human input where there's a ton of variability/decision-making in both the E and the T, and only the L is known in advance.

[@daveebbelaar]

Good point. In those cases, agentic workflows make perfect sense.

[@ShpanMan]

@@daveebbelaar So you admit your title is extremely silly.

[@EmilioGagliardi]

Interesting. Working on a CrewAI project atm and I found I was using a DAG approach to tasks because of my experience with Kedro. One task, one transformation, one output and keep working sequentially. In a nutshell, you're describing Kedro's approach and philosophy. Its just not fine-tuned for generative AI use cases yet. What I've found with multi agent apps is that I end up building tools that do all the heavy lifting and the agent Is used to generate a piece of data (like a query string) used in subsequent processing. The challenge is building guardrails to prevent an agent from going off the reservation when something doesn't work. If you give an agent access to a tool as simple as a search tool, if it gets stuck, it could end up calling the tool in a loop and there goes your credits. So we're still having to treat agents like toddlers... would be interesting to see your take on kedro.

[@hailrider8188]

Cyclical/recursive algorithms are needed for many problems which in part, is what agentic frameworks attempt solve. Your sequential processing only paradigm is applicable only to certain problems.

[@micbab-vg2mu]

Great video - thank you for sharing:)

[@LuisBorges0]

AutoGen and CrewAI I think are more experiments than anything else. I use Python as a Maestro... then some AI, when I need it's generative abilities, some playwright when I need web automation... but business run on logic not in a democratic way and not in a creative way. They incorporate creativity in some steps but that's just that. A procedure is the only way to go since ever or else we'll have unpredictability that goes against efficiency and bad for processes in business, in factories, and would make it impossible to have any type of Quality Control.

[@michaelirey]

While your critique of agent frameworks is spot on and compelling, it seems there's a misconception about their potential. Your custom system resembles langchain+langgraph, highlighting a need for deeper understanding before dismissing existing frameworks.

[@JohnDoe-m6s]

@13:33 Please create an in-depth video on these concepts using the example that you are showing in this video !

[@Crates-Media]

Here's something you can help me understand, as an intermediate-level coder learning all of the nuances of AI/ML and their applcations. You're extolling the value of the directed acyclic graph approach towards data processing pipelines, to avoid sending data to earlier stages. As a fan of idempotency and functional programming, I _think_ that I somewhat understand where you're coming from in your premise. But in my studies of models, I'm also seeing a lot of buzz around the differentiation between methodologies of KANs vs MLPs. My question is this: wouldn't there be some value in using information uncovered later in the pipeline to refine what you're doing earlier on? For instance, let's say you're entertaining guests, and planning to serve appetizers. A very early step might be purchasing ingredients. Later on, you realize that not all of the guests show up. If we're just going to keep moving forward, we make more appetizers than are needed. The alternative: when less guests show up or RSVP, instead of making as many apps as your ingredients/plans dictate, you make less. Now you have less appetizers and you store or freeze the ingredients you didn't use. You _could_ make them, and freeze the unused portions. But by sending the information collected later back to an earlier step, you instead have the raw ingredients to use in other recipes instead. This is a really lousy and forced metaphor, but it's all I could come up with off the top of my head. It just seems like there's value in the concept. On a different level, isn't this just sort of a form of backpropagation? The ability to reinform earlier calculations with the results of later ones?

[@Rusputin-eo6tv]

framework in the end is just how you organize your code.

[@mtrifiro]

Thank you for putting into words exactly what I had been feeling when trying these agent tools.

[@ShpanMan]

You are just looking for reasons why your career is not ending 🤣 Just accept it and be thankful AI will do all the work for us in a few years.

[@mikekidder]

state machines, state machines, state machines...

[@scottc8274]

I think this is fair, for linear data flows and data transforms simplicity is best but for complex, multiphase transactional goals which contain conditional variables which could change during the flow you need a controller/manager which can identify and respond to these state changes. In addition ideally an orchestration agent wouldn't be bound to a set of pre agreed agents/services/API s but would be constantly optimising its flow - searching out better ways to reach its outcome. A solid Agentic framework should be able to switch between and explicit and discoverable transformation approach hopefully..

[@Bartskol]

This is exactly what i needed.

[@333SFx]

Langgraph

[@toi500]

One of the worst videos I've seen this year. You ignored a very capable framework like LangGraph. Also, the fact you did not talk about hierarchical or sequential agent workflows and you ONLY focused in the multi-agent ones to present us "your case" is not serious. In any event, even a simple multi-agent graph can achieve a 100% success rate in resolving tickets. You don't need to hardcode all those steps at all.

[@jasonsuarez6439]

I thought the same starting the video but I ended up appreciating the effort to take a step back to first principles. It's refreshing and at least make us think about choosing a framework. If he was really dishonest, I could have talked about the biggest problem of those agentic frameworks... latency at scale

[@daveebbelaar]

I might be too ignorant here, but I haven't seen any of those frameworks implemented in production yet. I'm aware that I just scratched the surface here and that there are indeed many different agent workflows. My point is not to discard all of them but rather to mention that, based on my experience, for most business automation problems, building your own pipeline from scratch is typically better. What's your experience with agent frameworks, and do you have a good production-ready use case? I'd love to learn more about this because I really want to like them!

[@ShpanMan]

@@daveebbelaar Not a single person thinks Agents are ready for production. It's an early implementation prototype that gets better and better with each new AI model that comes out. In a few years it will crush any human and send you to retirement (in a good way).

[@opensky1710]

My problem is having hard time to find girl friends.

[@denisblack9897]

Another day - another wave of similar videos… I did not get this agents bullshit from the start(felt like data gathering scams to me), cuz i was chaining prompts when chatgpt came out. You gonna chain prompts manually if you are building something more than a useless demo.

[@mrprogamer896]

could you explain more about "chaining prompts manually"? Everything about agents seems confusing to me tbh.

[@ali-om4uv]

Really cool idea!! You need to get rid of your erat8c teaching style tho. Especially with respect to quickly showing pictures jump g to Code etc.... Anyhow.... I would love to See the full fledged videos!!

[@daveebbelaar]

Thank you for the tip! Normally, my tutorials are structured and step-by-step. However, this one was not intended to be like that. But it's good to know! I will most likely publish a video on this approach in the near future.

[@SiyaNtombela]

Such a weird take