4 ways loop engineering fails (and how to fix them) Transcript, AI Summary & Key Points

00:00 In this video, you'll learn about four ways loop engineering fails and how to fix them. So, if you've been following AI developer trends, maybe you've heard about loop engineering, but Annie, what is loop engineering? >> Yeah, that is a good question. You can think of it as you replacing yourself with a system. So, instead of you keep typing and trying to solve a problem, you create a system and set a goal and let the system keep retrying until it's meeting the goal.

00:25 And that whole process and that system you can think of as a loop engineering. >> That sounds really useful until it's not. Let's talk about four common ways that loops tend to break down. Yeah. >> The first is runaway loops. Have you ever written a runaway loop? >> Yeah, of course. You know once that I'm trying to create a retry logic and I just put all the retry logic in a loop and I forget to add exit condition and then my application just froze and I was like what's going on?

00:50 I realized oh I it run into the infinite loop. It's like a classic developer experience that many people run into when they're first learning to code. But the problem is now that we're in the age of AI, you're not just burning through memory. You're not just crashing your stack. You're burning tokens and those cost real money. >> What do we do? >> So that is why a clear stop rule is very important.

01:12 For example, you can set like maximum iteration number to be five to stop the loop or you you set a time limit or set a limit on the token or the call so that it won't burn your token. That's pretty much the first uh failure pattern. The second failure pattern is the uh inverified autonomy. So what does it mean is think of you have a agent and you let the agent do a certain job and in the same conversation you ask the same agent to do the same job again and then because it's already do the job before so it has a memory

01:42 has a context and then later on the job uh the agent tend to reuse the context reuse the memory it has before. But what if in the first try it made a mistake? what if it's not a good data set to reuse and that way you're not like getting a good answer if you just keep running something in the loop. So this is also what we call confirmation bias. >> Yeah.

02:02 And in addition to confirmation bias and context pollution, there's just plainly an issue with asking an agent to evaluate its own work. It's like asking a kindergartner to grade its own homework. No, we don't want to do that. >> Instead, you can have agent A >> evaluating work for agent B and vice versa. And that way we have a separation of concerns.

02:20 Yeah. And it's just cleaner and more accurate. >> So you can have LM as a judge or you can set a clear matrix to evaluate your system. What is the third common pattern? >> The third issue comes from uncheckable goals. >> Oh, what is that? >> That is if I for example asked a loop engineering workflow to make this summary better. >> Okay, what is better?

02:40 That's completely vague and the LLM is just going to crash out trying to understand what we mean by that. >> Yeah. So in this scenario you should have a explicit and checkable criteria. So what I mean by checkable means it's uh non-debatable non-negotiable. For example, it has to be less than 10 word counts. It's very clear or it has a zero compilation errors things like that.

03:03 What are the last common failure we have? >> The fourth failure mode is complexity. So if I asked a single loop to make me a one paragraph summary, it'd probably do pretty okay. But if I asked a single loop, hey, I need a 50-page PDF with a bunch of generated images and tables, it would probably choke. So if we need a large, you know, complex problem like that, what's a better way of handling it?

03:26 >> So that is the best time for us to move from loop engineering to graph engineering. So instead of you have one agent doing the one running process, right? You have a organization chart, you have this whole graph workflow to orchestrate everything. You can put loop as part of the graph or you can create your own nodes and edges to design your own system and that way you can add more control and you can handle more complex situation like we just mentioned before.

03:52 >> Very cool. >> If you want to learn more about graph engineering, you can check out Annie's tutorial. So to summarize, loop engineering is very useful for contained, verifiable, and repetitive tasks. To keep your loops running smoothly, always enforce stop rules and cost caps. Never let an agent rubber stamp its own work. Define clear, verifiable goals.

04:12 And when single loops hit context limits, orchestrate them into graphs. >> Yeah. And thank you so much for watching this video. And if you have faced any failure trying loop engineering or you have any other experience with loop engineer or graph engineering, feel free to share them in the comments and we will see you in the next video. Bye >> bye. >> [music]