1 00:00:00,000 --> 00:00:02,360 All right, so today we're diving deep into 2 00:00:02,360 --> 00:00:06,080 practical considerations for a Gentic LLM system. 3 00:00:06,080 --> 00:00:07,240 Sounds intriguing. 4 00:00:07,240 --> 00:00:10,400 It is, it was written by Chris Cyford and Vyshak Bell 5 00:00:10,400 --> 00:00:11,240 Ah, yeah. 6 00:00:11,240 --> 00:00:12,800 from the University of Edinburgh. 7 00:00:12,800 --> 00:00:16,880 And this paper, well, it tackles the whole idea 8 00:00:16,880 --> 00:00:20,360 of using LLMs, you know, large language modders, 9 00:00:20,360 --> 00:00:23,000 as the brains behind autonomous agents. 10 00:00:23,000 --> 00:00:24,280 Okay, so not just chatbots, 11 00:00:24,280 --> 00:00:26,240 like things that can actually go out and do things. 12 00:00:26,240 --> 00:00:28,800 Yeah, exactly, like real action, not just words. 13 00:00:28,800 --> 00:00:30,880 Gotcha, that's a pretty hot topic right now. 14 00:00:30,880 --> 00:00:32,280 What's unique about this paper? 15 00:00:32,280 --> 00:00:33,480 Well, that's what's really cool about it. 16 00:00:33,480 --> 00:00:37,680 It bridges the gap between what academic researchers 17 00:00:37,680 --> 00:00:38,960 are all excited about, 18 00:00:38,960 --> 00:00:41,360 and then what's actually happening out there in the industry. 19 00:00:41,360 --> 00:00:42,680 Okay, so are we talking like, 20 00:00:42,680 --> 00:00:45,840 are the academics thinking of, you know, 21 00:00:45,840 --> 00:00:48,360 robot butlers powered by these LLMs 22 00:00:48,360 --> 00:00:50,120 while companies are just trying to like, 23 00:00:50,120 --> 00:00:52,520 I don't know, automate their customer service calls? 24 00:00:52,520 --> 00:00:54,880 Maybe not that extreme, but yeah, 25 00:00:54,880 --> 00:00:57,000 you're definitely picking up in the core difference there. 26 00:00:57,000 --> 00:00:57,840 Okay. 27 00:00:57,840 --> 00:01:01,640 So academics, they're looking at LLMs from a very, 28 00:01:01,640 --> 00:01:04,120 I guess you could say theoretical perspective. 29 00:01:04,120 --> 00:01:06,800 Thinking about things like, you know, beliefs, 30 00:01:06,800 --> 00:01:11,080 reasoning, planning, all those kind of high level things 31 00:01:11,080 --> 00:01:14,360 we associate with, you know, intelligent agents. 32 00:01:14,360 --> 00:01:17,800 So they're trying to figure out if an LLM could like, 33 00:01:17,800 --> 00:01:21,480 in theory, become this truly independent agent, 34 00:01:21,480 --> 00:01:23,120 you know, one that can make its own decisions 35 00:01:23,120 --> 00:01:25,720 and actually act on them in the real world. 36 00:01:25,720 --> 00:01:27,040 Yeah, fascinating stuff. 37 00:01:27,040 --> 00:01:29,960 But on the other side, you have the industry folks 38 00:01:29,960 --> 00:01:32,160 who are much more focused on, well, how can we use this? 39 00:01:32,160 --> 00:01:33,040 How can we apply it? 40 00:01:33,040 --> 00:01:35,200 How can this make us money, I assume. 41 00:01:35,200 --> 00:01:38,480 So they're asking more like, can we use LLMs 42 00:01:38,480 --> 00:01:42,360 to build systems that can actually solve specific problems? 43 00:01:42,360 --> 00:01:47,040 Like planning a trip, managing a project, you know, 44 00:01:47,040 --> 00:01:48,400 very concrete things. 45 00:01:48,400 --> 00:01:49,840 So less existential pondering, 46 00:01:49,840 --> 00:01:51,080 more about getting stuff done. 47 00:01:51,080 --> 00:01:52,360 Exactly, yeah. 48 00:01:52,360 --> 00:01:56,000 They view the LLM as, okay, this is the core reasoning engine. 49 00:01:56,000 --> 00:01:56,840 Okay. 50 00:01:56,840 --> 00:01:58,560 And then they're gonna add on these different modules 51 00:01:58,560 --> 00:02:01,640 for things like how to plan, maybe some memory. 52 00:02:01,640 --> 00:02:03,920 And then how it can interface with different tools. 53 00:02:03,920 --> 00:02:07,280 So this lets the LLM act as this agent 54 00:02:07,280 --> 00:02:10,400 in a more, I guess you could say, concrete way. 55 00:02:10,400 --> 00:02:12,400 Okay, I'm starting to see the distinction here. 56 00:02:12,400 --> 00:02:15,320 And you know what, to make it a little bit more tangible, 57 00:02:15,320 --> 00:02:20,120 the paper uses this example of a pescatarian meal assistant. 58 00:02:20,120 --> 00:02:21,080 Oh, interesting. 59 00:02:21,080 --> 00:02:22,360 What is that all about? 60 00:02:22,360 --> 00:02:24,800 That's a really great way to kind of illustrate 61 00:02:24,800 --> 00:02:29,720 the challenges and also the potential of these LLM agents. 62 00:02:29,720 --> 00:02:34,400 So imagine an LLM that's helping someone who's pescatarian. 63 00:02:34,400 --> 00:02:39,000 So they only eat fish and seafood to plan out their meals. 64 00:02:39,000 --> 00:02:43,480 So this agent, it has to be able to search recipes, 65 00:02:43,480 --> 00:02:46,480 maybe consider allergies or dietary restrictions, 66 00:02:46,480 --> 00:02:49,080 figure out if ingredients are even available, 67 00:02:49,080 --> 00:02:51,520 maybe even order those groceries for the person. 68 00:02:51,520 --> 00:02:53,080 Yeah, that's definitely way more complex 69 00:02:53,080 --> 00:02:55,040 than just like pulling up a recipe on the internet. 70 00:02:55,040 --> 00:02:57,360 Like it needs to understand what the user wants, 71 00:02:57,360 --> 00:02:59,000 then adapt to changing situations, 72 00:02:59,000 --> 00:03:00,520 maybe some ingredients aren't available, 73 00:03:00,520 --> 00:03:03,200 and then interact with all these different tools 74 00:03:03,200 --> 00:03:04,560 to actually get that done. 75 00:03:04,560 --> 00:03:06,120 And that's where the paper really gets 76 00:03:06,120 --> 00:03:07,400 into the nitty gritty. 77 00:03:07,400 --> 00:03:10,640 It goes into all these very specific considerations 78 00:03:10,640 --> 00:03:12,280 for building these kinds of agents. 79 00:03:12,280 --> 00:03:15,000 And it starts with what might seem like a simple thing, 80 00:03:15,000 --> 00:03:17,960 but it's surprisingly tricky and that's planning. 81 00:03:17,960 --> 00:03:19,000 Wait, tricky. 82 00:03:19,000 --> 00:03:23,880 I thought LLMs were supposed to be amazing at planning. 83 00:03:23,880 --> 00:03:26,080 If they can write code, compose poetry, 84 00:03:26,080 --> 00:03:27,760 surely they can handle a meal plan, right? 85 00:03:27,760 --> 00:03:29,560 You would think, right. 86 00:03:29,560 --> 00:03:32,000 But research actually suggests that LLMs 87 00:03:32,000 --> 00:03:36,000 aren't naturally gifted at this kind of step-by-step logic 88 00:03:36,000 --> 00:03:36,840 of planning. 89 00:03:36,840 --> 00:03:37,680 Interesting. 90 00:03:37,680 --> 00:03:40,440 So they're great at understanding and generating language, 91 00:03:40,440 --> 00:03:43,120 but this logic seems to be a weak spot. 92 00:03:43,120 --> 00:03:46,160 So our little pescatarian meal assistant 93 00:03:46,160 --> 00:03:49,720 might be awesome at finding the most delicious fish recipes. 94 00:03:49,720 --> 00:03:51,400 But then when it comes to actually figuring out 95 00:03:51,400 --> 00:03:54,160 the steps to buy the ingredients, cook the meal, 96 00:03:54,160 --> 00:03:55,440 it kind of falls apart. 97 00:03:55,440 --> 00:03:56,800 Yeah, that's the problem. 98 00:03:56,800 --> 00:04:00,800 And so the paper suggests that instead of trying to force 99 00:04:00,800 --> 00:04:03,760 these LLMs to become master planners, 100 00:04:03,760 --> 00:04:07,000 we should focus on this idea of task decomposition. 101 00:04:07,000 --> 00:04:08,680 Cask decomposition. 102 00:04:08,680 --> 00:04:10,480 OK, is that AI therapy? 103 00:04:10,480 --> 00:04:12,080 We're going to help the LLM break down 104 00:04:12,080 --> 00:04:14,160 its problems into these manageable chunks. 105 00:04:14,160 --> 00:04:15,520 It's a really good analogy. 106 00:04:15,520 --> 00:04:18,680 So instead of asking the LLM to plan 107 00:04:18,680 --> 00:04:20,880 an entire week's worth of meals, 108 00:04:20,880 --> 00:04:23,080 you break it down into smaller steps. 109 00:04:23,080 --> 00:04:26,600 So find recipes, create the shopping list, 110 00:04:26,600 --> 00:04:28,600 figure out when to cook things. 111 00:04:28,600 --> 00:04:31,920 And this makes each task much simpler. 112 00:04:31,920 --> 00:04:34,160 And it plays to the LLMs' strengths. 113 00:04:34,160 --> 00:04:37,000 So it's about designing the task to fit the LLM, not 114 00:04:37,000 --> 00:04:37,680 the other way around. 115 00:04:37,680 --> 00:04:38,640 Exactly, yeah. 116 00:04:38,640 --> 00:04:41,280 And the authors, they argue that this is actually 117 00:04:41,280 --> 00:04:43,920 way more effective than trying to use 118 00:04:43,920 --> 00:04:47,320 a less powerful LLM for this complex planning. 119 00:04:47,320 --> 00:04:50,000 So it's like starting with a high performance engine 120 00:04:50,000 --> 00:04:53,240 and adjusting the workload rather than trying to soup up 121 00:04:53,240 --> 00:04:54,520 like a weaker engine. 122 00:04:54,520 --> 00:04:55,280 Exactly. 123 00:04:55,280 --> 00:04:56,240 That makes sense. 124 00:04:56,240 --> 00:04:58,960 But OK, planning is just one piece of the puzzle. 125 00:04:58,960 --> 00:04:59,880 What about memory? 126 00:04:59,880 --> 00:05:03,760 How do we ensure that our little LLM agent actually 127 00:05:03,760 --> 00:05:07,320 remembers these crucial pieces of information, 128 00:05:07,320 --> 00:05:11,560 like dietary restrictions or favorite recipes, all that stuff? 129 00:05:11,560 --> 00:05:12,440 Yeah, great question. 130 00:05:12,440 --> 00:05:15,080 And that's where something called retrieval augmented 131 00:05:15,080 --> 00:05:16,280 generation comes in. 132 00:05:16,280 --> 00:05:16,760 OK. 133 00:05:16,760 --> 00:05:17,920 Or REG, for short. 134 00:05:17,920 --> 00:05:18,560 REG. 135 00:05:18,560 --> 00:05:20,840 And it's basically a technique that 136 00:05:20,840 --> 00:05:25,480 gives the LLM access to these external information sources. 137 00:05:25,480 --> 00:05:27,000 So like databases, stuff like that? 138 00:05:27,000 --> 00:05:29,600 Yeah, databases, knowledge graphs that 139 00:05:29,600 --> 00:05:31,960 are relevant to the task at hand. 140 00:05:31,960 --> 00:05:34,560 So instead of relying only on what 141 00:05:34,560 --> 00:05:39,000 it learned during its initial training phase, 142 00:05:39,000 --> 00:05:41,080 it can consult a cheat sheet. 143 00:05:41,080 --> 00:05:41,560 Right. 144 00:05:41,560 --> 00:05:42,960 Like with curated information. 145 00:05:42,960 --> 00:05:43,600 You got it. 146 00:05:43,600 --> 00:05:43,920 OK. 147 00:05:43,920 --> 00:05:45,840 So for our pescatarian meal assistant, 148 00:05:45,840 --> 00:05:49,440 this could be, I don't know, a database of pescatarian recipes, 149 00:05:49,440 --> 00:05:50,840 information about allergies. 150 00:05:50,840 --> 00:05:51,400 Makes sense. 151 00:05:51,400 --> 00:05:54,440 Or even feedback from the user on past meals, 152 00:05:54,440 --> 00:05:56,520 like what they liked, what they didn't like. 153 00:05:56,520 --> 00:05:58,000 So this seems like a really smart way 154 00:05:58,000 --> 00:06:02,280 to prevent those hallucinations we always hear about with LLMs, 155 00:06:02,280 --> 00:06:05,280 where they just like spout out completely made up stuff. 156 00:06:05,280 --> 00:06:06,080 Yeah, exactly. 157 00:06:06,080 --> 00:06:07,840 Or inaccurate information. 158 00:06:07,840 --> 00:06:12,320 Our RAG helps ground the LLM in reliable data, 159 00:06:12,320 --> 00:06:14,720 so it's less likely to go off the rails. 160 00:06:14,720 --> 00:06:16,720 And it also keeps that information up to date, 161 00:06:16,720 --> 00:06:18,600 which is super important, especially for something 162 00:06:18,600 --> 00:06:19,640 like meal planning, right? 163 00:06:19,640 --> 00:06:21,800 So trends and ingredients are always changing. 164 00:06:21,800 --> 00:06:22,300 Right. 165 00:06:22,300 --> 00:06:24,480 So no more AI recommending outdated recipes 166 00:06:24,480 --> 00:06:26,000 or suggesting you use ingredients 167 00:06:26,000 --> 00:06:27,040 that you can't find anywhere. 168 00:06:27,040 --> 00:06:27,840 Yeah, exactly. 169 00:06:27,840 --> 00:06:28,400 That's great. 170 00:06:28,400 --> 00:06:30,840 It's like giving your LLM agent a subscription 171 00:06:30,840 --> 00:06:33,640 to all the latest food blogs and culinary magazines. 172 00:06:33,640 --> 00:06:34,140 Yeah. 173 00:06:34,140 --> 00:06:36,320 Keeps its knowledge fresh and relevant. 174 00:06:36,320 --> 00:06:37,040 I like that. 175 00:06:37,040 --> 00:06:37,320 Yeah. 176 00:06:37,320 --> 00:06:39,760 But what about remembering things that aren't necessarily 177 00:06:39,760 --> 00:06:43,120 in a database, so like specific user preferences 178 00:06:43,120 --> 00:06:45,920 or feedback from past interactions? 179 00:06:45,920 --> 00:06:48,920 Do we need to give the LLM a digital diary 180 00:06:48,920 --> 00:06:50,000 to keep track of all that? 181 00:06:50,000 --> 00:06:50,500 Right. 182 00:06:50,500 --> 00:06:53,240 So that brings us to the concept of long term memory 183 00:06:53,240 --> 00:06:54,760 for these LLM agents. 184 00:06:54,760 --> 00:06:55,760 OK, interesting. 185 00:06:55,760 --> 00:06:59,920 It's about storing key takeaways from those past interactions, 186 00:06:59,920 --> 00:07:02,880 and then they can use those to inform future decisions. 187 00:07:02,880 --> 00:07:06,880 So things like allergies, dislikes, maybe even just 188 00:07:06,880 --> 00:07:08,480 like a preferred cooking style. 189 00:07:08,480 --> 00:07:11,600 It's all about building that personalized experience. 190 00:07:11,600 --> 00:07:12,480 Exactly. 191 00:07:12,480 --> 00:07:15,800 And the paper, it suggests focusing 192 00:07:15,800 --> 00:07:19,320 on storing information that is independent, 193 00:07:19,320 --> 00:07:21,720 so it's not tied to a specific event. 194 00:07:21,720 --> 00:07:25,720 But it kind of reflects this general preference or constraint. 195 00:07:25,720 --> 00:07:28,680 It also needs to be consistently relevant to the agent's 196 00:07:28,680 --> 00:07:31,760 purpose and applicable over the long term. 197 00:07:31,760 --> 00:07:33,620 So instead of remembering, the user 198 00:07:33,620 --> 00:07:36,360 didn't like the salmon recipe on Tuesday, 199 00:07:36,360 --> 00:07:38,640 it'd be more like the user disliked salmon. 200 00:07:38,640 --> 00:07:39,520 Exactly. 201 00:07:39,520 --> 00:07:42,160 And that's relevant to any meal planning, 202 00:07:42,160 --> 00:07:44,320 not just a specific instance. 203 00:07:44,320 --> 00:07:45,200 That's a great summary. 204 00:07:45,200 --> 00:07:48,680 It's like you're building a knowledge base of the user's 205 00:07:48,680 --> 00:07:52,280 core needs and values, which the LLM can then 206 00:07:52,280 --> 00:07:54,880 draw on to make those more personalized and accurate 207 00:07:54,880 --> 00:07:55,400 decisions. 208 00:07:55,400 --> 00:07:56,840 This is fascinating. 209 00:07:56,840 --> 00:07:59,560 But so far, we've mostly been talking about these LLMs just 210 00:07:59,560 --> 00:08:02,280 interacting with text and data. 211 00:08:02,280 --> 00:08:05,640 But how do they actually do things in the real world? 212 00:08:05,640 --> 00:08:07,960 Can they really interact with our physical environment? 213 00:08:07,960 --> 00:08:08,240 Right. 214 00:08:08,240 --> 00:08:09,960 And that's where tools come in. 215 00:08:09,960 --> 00:08:13,560 Tools are essentially extensions that give the LLMs 216 00:08:13,560 --> 00:08:16,680 the ability to go beyond text and actually interact 217 00:08:16,680 --> 00:08:19,560 with other systems or even physical devices. 218 00:08:19,560 --> 00:08:22,080 So we're giving these LLMs superpowers, 219 00:08:22,080 --> 00:08:24,880 like to search the web, send emails, 220 00:08:24,880 --> 00:08:26,640 control our smart appliances. 221 00:08:26,640 --> 00:08:27,440 Exactly. 222 00:08:27,440 --> 00:08:27,800 Yeah. 223 00:08:27,800 --> 00:08:29,880 So for our meal assistant, a tool 224 00:08:29,880 --> 00:08:33,800 might be something that lets it search for recipes online, 225 00:08:33,800 --> 00:08:36,800 maybe filter by specific ingredients or dietary 226 00:08:36,800 --> 00:08:37,920 restrictions. 227 00:08:37,920 --> 00:08:39,520 Another tool could be something that 228 00:08:39,520 --> 00:08:41,000 lets it order groceries. 229 00:08:41,000 --> 00:08:41,440 Wow. 230 00:08:41,440 --> 00:08:41,960 Hold on. 231 00:08:41,960 --> 00:08:45,560 So the LLM could actually make purchases on behalf of the user. 232 00:08:45,560 --> 00:08:46,360 Yeah. 233 00:08:46,360 --> 00:08:47,960 That's taking this to a whole new level. 234 00:08:47,960 --> 00:08:48,520 It is. 235 00:08:48,520 --> 00:08:51,800 And it really highlights the, I guess you could say, 236 00:08:51,800 --> 00:08:54,520 the vast potential of these LLM agents. 237 00:08:54,520 --> 00:08:55,040 Yeah. 238 00:08:55,040 --> 00:08:57,760 But as we develop these more complex systems, 239 00:08:57,760 --> 00:09:00,240 the number of tools is just going to explode. 240 00:09:00,240 --> 00:09:00,740 Right. 241 00:09:00,740 --> 00:09:05,160 So we're building this AI Swiss Army knife with a tool 242 00:09:05,160 --> 00:09:06,960 for every possible situation. 243 00:09:06,960 --> 00:09:09,520 And that brings us to the very crucial issue 244 00:09:09,520 --> 00:09:10,440 of tool management. 245 00:09:10,440 --> 00:09:11,640 Right, because we don't want the AI 246 00:09:11,640 --> 00:09:13,520 to get lost in its own digital toolbox. 247 00:09:13,520 --> 00:09:14,440 Yes, exactly. 248 00:09:14,440 --> 00:09:14,840 Yeah. 249 00:09:14,840 --> 00:09:16,880 And so the paper, it stresses the importance 250 00:09:16,880 --> 00:09:19,080 of clear documentation. 251 00:09:19,080 --> 00:09:21,400 Organization, almost like you're creating an instruction 252 00:09:21,400 --> 00:09:22,920 manual for each tool. 253 00:09:22,920 --> 00:09:23,420 Right. 254 00:09:23,420 --> 00:09:26,320 So we're not just throwing a bunch of tools at the AI 255 00:09:26,320 --> 00:09:27,760 and just hoping for the best. 256 00:09:27,760 --> 00:09:28,080 Right. 257 00:09:28,080 --> 00:09:31,280 We're carefully curating them, documenting them, 258 00:09:31,280 --> 00:09:33,640 just like we would with any complex software system. 259 00:09:33,640 --> 00:09:34,320 Exactly. 260 00:09:34,320 --> 00:09:37,160 It's all about setting the LLM agent up for success 261 00:09:37,160 --> 00:09:39,680 by making sure it has the right tools. 262 00:09:39,680 --> 00:09:40,120 Right. 263 00:09:40,120 --> 00:09:42,560 And that it knows how to use them effectively. 264 00:09:42,560 --> 00:09:46,680 OK, so we've got planning, memory, tools. 265 00:09:46,680 --> 00:09:49,720 What other considerations are there 266 00:09:49,720 --> 00:09:51,600 when we're building these LLM agents? 267 00:09:51,600 --> 00:09:54,640 Well, another really critical one is control flow. 268 00:09:54,640 --> 00:09:55,160 OK. 269 00:09:55,160 --> 00:09:57,920 And that's basically the decision-making process 270 00:09:57,920 --> 00:09:59,120 of the LLM agent. 271 00:09:59,120 --> 00:10:01,400 So it's like the AI's internal traffic controller. 272 00:10:01,400 --> 00:10:02,520 That's a good analogy. 273 00:10:02,520 --> 00:10:04,560 OK, it's deciding which steps to take, 274 00:10:04,560 --> 00:10:07,200 when to use which tools, and when to deliver a result. 275 00:10:07,200 --> 00:10:08,080 Exactly, yeah. 276 00:10:08,080 --> 00:10:10,000 So effective control flow requires 277 00:10:10,000 --> 00:10:12,680 that the LLM agent understands all of its options. 278 00:10:12,680 --> 00:10:13,120 OK. 279 00:10:13,120 --> 00:10:16,520 The tools that it has, the personas that it can embody, 280 00:10:16,520 --> 00:10:18,640 and the possible actions it can take. 281 00:10:18,640 --> 00:10:20,280 And then based on that understanding, 282 00:10:20,280 --> 00:10:22,240 it can make decisions about how to proceed. 283 00:10:22,240 --> 00:10:22,760 Exactly. 284 00:10:22,760 --> 00:10:24,880 Just like a human would in a similar situation. 285 00:10:24,880 --> 00:10:25,720 Exactly. 286 00:10:25,720 --> 00:10:27,320 So back to our meal assistant. 287 00:10:27,320 --> 00:10:28,080 OK. 288 00:10:28,080 --> 00:10:31,720 The control flow would determine how it responds 289 00:10:31,720 --> 00:10:34,520 if a user says, plan my meals for next week. 290 00:10:34,520 --> 00:10:35,240 OK. 291 00:10:35,240 --> 00:10:38,120 So it might consult its memory about, OK, 292 00:10:38,120 --> 00:10:39,480 what's this person's diet? 293 00:10:39,480 --> 00:10:40,000 OK. 294 00:10:40,000 --> 00:10:44,840 Maybe use a planning module to create a kind of draft schedule. 295 00:10:44,840 --> 00:10:45,480 Right. 296 00:10:45,480 --> 00:10:49,960 And then it could use a tool to maybe check the user's calendar 297 00:10:49,960 --> 00:10:51,280 for any upcoming events. 298 00:10:51,280 --> 00:10:53,520 It's like a well-choreographed dance 299 00:10:53,520 --> 00:10:56,040 where each step flows smoothly into the next. 300 00:10:56,040 --> 00:10:56,840 Yeah, exactly. 301 00:10:56,840 --> 00:10:57,640 That's really cool. 302 00:10:57,640 --> 00:11:00,200 And that smooth flow is really essential for creating 303 00:11:00,200 --> 00:11:02,800 a seamless and efficient user experience. 304 00:11:02,800 --> 00:11:05,440 But what happens when things don't go according to plan? 305 00:11:05,440 --> 00:11:05,960 Right. 306 00:11:05,960 --> 00:11:08,880 Because let's be honest, with all this complexity, 307 00:11:08,880 --> 00:11:10,760 things are bound to go wrong at some point. 308 00:11:10,760 --> 00:11:12,560 LLMs, they're not perfect. 309 00:11:12,560 --> 00:11:13,000 Right. 310 00:11:13,000 --> 00:11:15,280 Neither are the tools and systems they're interacting with. 311 00:11:15,280 --> 00:11:15,840 Exactly. 312 00:11:15,840 --> 00:11:19,200 And that brings us to another critical consideration. 313 00:11:19,200 --> 00:11:20,520 Error handling. 314 00:11:20,520 --> 00:11:21,160 OK. 315 00:11:21,160 --> 00:11:21,840 Error handling. 316 00:11:21,840 --> 00:11:25,200 So are we giving the AI like a panic button? 317 00:11:25,200 --> 00:11:26,160 Not quite. 318 00:11:26,160 --> 00:11:29,560 It's more about anticipating potential issues 319 00:11:29,560 --> 00:11:31,920 and then coming up with strategies to address them. 320 00:11:31,920 --> 00:11:32,760 OK. 321 00:11:32,760 --> 00:11:37,480 So one approach is to simply retry a failed operation. 322 00:11:37,480 --> 00:11:38,080 OK. 323 00:11:38,080 --> 00:11:42,520 But maybe with a slightly different kind of seed, 324 00:11:42,520 --> 00:11:46,640 which can nudge the LLM toward a different output. 325 00:11:46,640 --> 00:11:49,240 So we're giving it a second chance, 326 00:11:49,240 --> 00:11:50,800 but with a little tweak to its thinking. 327 00:11:50,800 --> 00:11:52,240 Yeah, exactly. 328 00:11:52,240 --> 00:11:56,280 Another approach is to give the LLM more context about the error. 329 00:11:56,280 --> 00:11:56,600 OK. 330 00:11:56,600 --> 00:11:59,120 So maybe like a specific error message 331 00:11:59,120 --> 00:12:01,520 or some additional information that might help it 332 00:12:01,520 --> 00:12:03,000 understand what went wrong. 333 00:12:03,000 --> 00:12:05,960 So it's like we're giving the AI a bug report 334 00:12:05,960 --> 00:12:07,640 so it can try to debug itself. 335 00:12:07,640 --> 00:12:08,720 That's a good way to put it, yeah. 336 00:12:08,720 --> 00:12:09,120 OK. 337 00:12:09,120 --> 00:12:10,760 And if the LLM is still stumped, 338 00:12:10,760 --> 00:12:12,440 we can even bring in another LLM, 339 00:12:12,440 --> 00:12:16,120 maybe one that specializes in debugging or error correction. 340 00:12:16,120 --> 00:12:18,800 Oh, so it's like calling in the AI cavalry 341 00:12:18,800 --> 00:12:20,920 to rescue a mission that's gone off the rails. 342 00:12:20,920 --> 00:12:21,600 Exactly. 343 00:12:21,600 --> 00:12:22,320 I like it. 344 00:12:22,320 --> 00:12:27,000 And this kind of multi-layered approach to error handling 345 00:12:27,000 --> 00:12:31,720 is super crucial for building those really robust and reliable 346 00:12:31,720 --> 00:12:34,880 LLM agents that can function in the real world. 347 00:12:34,880 --> 00:12:37,840 Because in the real world, unexpected things happen. 348 00:12:37,840 --> 00:12:40,000 This is all incredibly fascinating. 349 00:12:40,000 --> 00:12:43,520 And it's clear that building these effective LLM agents 350 00:12:43,520 --> 00:12:46,560 requires a lot of thought and planning. 351 00:12:46,560 --> 00:12:50,920 But I'm also struck by how much we're still figuring out 352 00:12:50,920 --> 00:12:51,760 about these systems. 353 00:12:51,760 --> 00:12:54,920 It's like we're explorers charting a new frontier, 354 00:12:54,920 --> 00:12:57,400 constantly discovering new possibilities and new challenges. 355 00:12:57,400 --> 00:12:58,040 Exactly. 356 00:12:58,040 --> 00:12:59,600 And that's what makes it so exciting. 357 00:12:59,600 --> 00:13:00,400 It is exciting. 358 00:13:00,400 --> 00:13:02,240 Speaking of complexity, I'm curious 359 00:13:02,240 --> 00:13:05,120 about how these different LLM agents handle 360 00:13:05,120 --> 00:13:08,240 the sheer volume of information that they have to process. 361 00:13:08,240 --> 00:13:10,560 It seems like it would be so easy for them to just get 362 00:13:10,560 --> 00:13:13,000 overwhelmed or lose track of important details. 363 00:13:13,000 --> 00:13:13,320 Right. 364 00:13:13,320 --> 00:13:16,320 And that brings us to this crucial issue of context 365 00:13:16,320 --> 00:13:16,840 management. 366 00:13:16,840 --> 00:13:17,480 Not text management. 367 00:13:17,480 --> 00:13:21,280 So just like humans, LLMs can get bogged down by too much 368 00:13:21,280 --> 00:13:22,200 information. 369 00:13:22,200 --> 00:13:23,640 Yeah, I feel that. 370 00:13:23,640 --> 00:13:28,200 And that can lead to errors, inefficiencies, 371 00:13:28,200 --> 00:13:30,040 and even nonsensical outputs. 372 00:13:30,040 --> 00:13:32,800 So we need to make sure we're not overloading these AIs 373 00:13:32,800 --> 00:13:34,600 with irrelevant data. 374 00:13:34,600 --> 00:13:35,040 Right. 375 00:13:35,040 --> 00:13:37,680 Or expecting them to remember every single thing 376 00:13:37,680 --> 00:13:39,120 from every bass interaction. 377 00:13:39,120 --> 00:13:40,040 Exactly, yeah. 378 00:13:40,040 --> 00:13:41,880 So the paper highlights the importance 379 00:13:41,880 --> 00:13:44,440 of trimming all that extraneous context, 380 00:13:44,440 --> 00:13:46,400 summarizing past conversations. 381 00:13:46,400 --> 00:13:48,440 And that can prevent the LLM from getting 382 00:13:48,440 --> 00:13:51,560 lost in just this sea of information. 383 00:13:51,560 --> 00:13:54,000 It's like decluttering the AI's workspace. 384 00:13:54,000 --> 00:13:55,880 Make sure it has the essential information, 385 00:13:55,880 --> 00:13:58,560 but not overwhelmed with all the unnecessary details. 386 00:13:58,560 --> 00:14:00,920 Yeah, it's like the difference between having a tidy desk 387 00:14:00,920 --> 00:14:02,840 with all your important documents organized 388 00:14:02,840 --> 00:14:05,760 versus just having a chaotic pile of papers everywhere. 389 00:14:05,760 --> 00:14:07,080 Right, exactly. 390 00:14:07,080 --> 00:14:08,720 OK, so effective context management 391 00:14:08,720 --> 00:14:11,320 is crucial for making sure the LLM agent can actually 392 00:14:11,320 --> 00:14:12,680 focus on the task at hand. 393 00:14:12,680 --> 00:14:13,040 Right. 394 00:14:13,040 --> 00:14:14,120 And make informed decisions. 395 00:14:14,120 --> 00:14:14,480 Yeah. 396 00:14:14,480 --> 00:14:16,440 And deliver those accurate results. 397 00:14:16,440 --> 00:14:17,080 Exactly. 398 00:14:17,080 --> 00:14:18,880 This is all incredibly insightful. 399 00:14:18,880 --> 00:14:20,880 But I'm also curious about how we actually 400 00:14:20,880 --> 00:14:23,640 evaluate the performance of these LLM agents. 401 00:14:23,640 --> 00:14:26,480 Is there some sort of AI performance review 402 00:14:26,480 --> 00:14:29,040 where we sit down and assess their skills 403 00:14:29,040 --> 00:14:30,040 and give them feedback? 404 00:14:30,040 --> 00:14:32,160 It's not quite as formal as that. 405 00:14:32,160 --> 00:14:32,660 OK. 406 00:14:32,660 --> 00:14:35,200 But evaluation is definitely a critical aspect 407 00:14:35,200 --> 00:14:37,160 of this development process. 408 00:14:37,160 --> 00:14:39,720 And the paper emphasizes the need 409 00:14:39,720 --> 00:14:42,800 to move beyond those traditional metrics, 410 00:14:42,800 --> 00:14:44,080 like just accuracy. 411 00:14:44,080 --> 00:14:44,440 OK. 412 00:14:44,440 --> 00:14:47,080 And focus more on real world performance. 413 00:14:47,080 --> 00:14:50,160 So instead of just checking if the AI got the right answer, 414 00:14:50,160 --> 00:14:52,360 we're actually looking at how it performs 415 00:14:52,360 --> 00:14:54,480 in a real world scenario. 416 00:14:54,480 --> 00:14:54,880 Right. 417 00:14:54,880 --> 00:14:56,520 With all the complexities and uncertainties. 418 00:14:56,520 --> 00:14:57,200 Exactly. 419 00:14:57,200 --> 00:15:00,720 So for our meal assistant, we might track things like, 420 00:15:00,720 --> 00:15:03,680 how many steps did it take to create that meal plan? 421 00:15:03,680 --> 00:15:04,080 OK. 422 00:15:04,080 --> 00:15:05,560 What tools did it use? 423 00:15:05,560 --> 00:15:05,920 OK. 424 00:15:05,920 --> 00:15:06,760 How long did it take? 425 00:15:06,760 --> 00:15:09,200 Even the cost of the ingredients that it selected. 426 00:15:09,200 --> 00:15:11,600 So it's not just about if it succeeds. 427 00:15:11,600 --> 00:15:11,960 Right. 428 00:15:11,960 --> 00:15:13,520 It's about how it succeeds. 429 00:15:13,520 --> 00:15:14,160 Exactly. 430 00:15:14,160 --> 00:15:17,560 We're looking at its process, its efficiency, 431 00:15:17,560 --> 00:15:21,000 its ability to adapt to unexpected situations. 432 00:15:21,000 --> 00:15:23,160 It's not just about creating the perfect meal plan 433 00:15:23,160 --> 00:15:24,320 in a lab setting. 434 00:15:24,320 --> 00:15:27,440 It's about testing its ability to function in the real world 435 00:15:27,440 --> 00:15:29,840 where things don't always go according to plan. 436 00:15:29,840 --> 00:15:30,720 Exactly. 437 00:15:30,720 --> 00:15:34,200 It's like giving the AI an internship in a real kitchen 438 00:15:34,200 --> 00:15:36,360 and seeing how it performs under pressure. 439 00:15:36,360 --> 00:15:37,600 That's a great way to put it. 440 00:15:37,600 --> 00:15:39,160 And it sounds like this evaluation 441 00:15:39,160 --> 00:15:41,880 requires a multifaceted approach. 442 00:15:41,880 --> 00:15:42,280 Yeah. 443 00:15:42,280 --> 00:15:44,680 That goes beyond those simple metrics. 444 00:15:44,680 --> 00:15:44,920 Right. 445 00:15:44,920 --> 00:15:46,560 And really gets into the nuances 446 00:15:46,560 --> 00:15:48,400 of that real world performance. 447 00:15:48,400 --> 00:15:50,920 And that brings us to another key consideration 448 00:15:50,920 --> 00:15:52,440 that the paper highlights. 449 00:15:52,440 --> 00:15:53,480 Model size. 450 00:15:53,480 --> 00:15:53,960 OK. 451 00:15:53,960 --> 00:15:56,960 Now, I'm not talking about how much physical space 452 00:15:56,960 --> 00:15:58,120 the AI takes up. 453 00:15:58,120 --> 00:15:58,520 Right. 454 00:15:58,520 --> 00:16:01,960 But the complexity and the scale of the LLM itself. 455 00:16:01,960 --> 00:16:02,160 Yeah. 456 00:16:02,160 --> 00:16:05,680 Model size is a major factor in determining its capabilities, 457 00:16:05,680 --> 00:16:07,480 its cost, its speed. 458 00:16:07,480 --> 00:16:09,600 So bigger models are more powerful, 459 00:16:09,600 --> 00:16:11,960 but they're also slower and more expensive to run. 460 00:16:11,960 --> 00:16:12,440 Exactly. 461 00:16:12,440 --> 00:16:12,760 Yeah. 462 00:16:12,760 --> 00:16:13,640 OK. 463 00:16:13,640 --> 00:16:16,840 And when you're designing these LLM agents, 464 00:16:16,840 --> 00:16:19,920 it can be tempting to start with a smaller, less 465 00:16:19,920 --> 00:16:21,280 expensive model. 466 00:16:21,280 --> 00:16:21,560 Right. 467 00:16:21,560 --> 00:16:22,960 It seems like the logical choice. 468 00:16:22,960 --> 00:16:25,960 Why splurge on this giant AI brain 469 00:16:25,960 --> 00:16:28,240 if a smaller one can do the job? 470 00:16:28,240 --> 00:16:29,960 That's what you might think. 471 00:16:29,960 --> 00:16:31,960 But the paper actually suggests the opposite. 472 00:16:31,960 --> 00:16:32,280 Wait. 473 00:16:32,280 --> 00:16:33,280 Seriously. 474 00:16:33,280 --> 00:16:36,600 Start with the biggest, baddest AI model available. 475 00:16:36,600 --> 00:16:38,160 That's the recommendation. 476 00:16:38,160 --> 00:16:40,280 And it makes sense when you consider that starting 477 00:16:40,280 --> 00:16:43,760 with a powerful model gives you that really high performance 478 00:16:43,760 --> 00:16:44,360 baseline. 479 00:16:44,360 --> 00:16:46,520 So it's like starting with the top of the line computer 480 00:16:46,520 --> 00:16:49,040 and then figuring out which components you can maybe 481 00:16:49,040 --> 00:16:51,680 downgrade without sacrificing performance. 482 00:16:51,680 --> 00:16:52,640 Yeah, exactly. 483 00:16:52,640 --> 00:16:55,040 So you can test different configurations, 484 00:16:55,040 --> 00:16:58,600 experiment with smaller models for maybe specific tasks, 485 00:16:58,600 --> 00:17:02,800 and really fine tune the system to optimize both performance 486 00:17:02,800 --> 00:17:03,560 and cost. 487 00:17:03,560 --> 00:17:04,960 So it's not about being cheap. 488 00:17:04,960 --> 00:17:05,240 Right. 489 00:17:05,240 --> 00:17:06,400 It's about being strategic. 490 00:17:06,400 --> 00:17:07,040 Exactly. 491 00:17:07,040 --> 00:17:08,920 And this actually brings up another point that's often 492 00:17:08,920 --> 00:17:11,440 overlooked when we're building these systems. 493 00:17:11,440 --> 00:17:13,640 And that's the need for integration 494 00:17:13,640 --> 00:17:15,360 with traditional engineering. 495 00:17:15,360 --> 00:17:17,360 OK, so now we're really blurring the lines here 496 00:17:17,360 --> 00:17:20,160 between computer science and AI. 497 00:17:20,160 --> 00:17:22,040 That's the beauty of this field. 498 00:17:22,040 --> 00:17:24,400 It's all about finding the best way 499 00:17:24,400 --> 00:17:28,880 to combine human ingenuity with these AI capabilities. 500 00:17:28,880 --> 00:17:31,760 So how do those traditional engineering practices 501 00:17:31,760 --> 00:17:34,760 fit into the world of LLM agents? 502 00:17:34,760 --> 00:17:38,120 Well, remember how we talked about the inherent randomness 503 00:17:38,120 --> 00:17:39,440 of LLMs? 504 00:17:39,440 --> 00:17:41,040 They can be a little unpredictable. 505 00:17:41,040 --> 00:17:42,880 Yeah, it's like they have a mind of their own, 506 00:17:42,880 --> 00:17:45,800 which can be amazing, but also a little unnerving sometimes. 507 00:17:45,800 --> 00:17:47,120 Yeah, exactly. 508 00:17:47,120 --> 00:17:50,080 And in certain situations, we need things to be predictable. 509 00:17:50,080 --> 00:17:51,440 We need them to be reliable. 510 00:17:51,440 --> 00:17:51,800 OK. 511 00:17:51,800 --> 00:17:54,600 And that's where traditional engineering techniques come in. 512 00:17:54,600 --> 00:17:58,080 So we can leverage those deterministic algorithms, 513 00:17:58,080 --> 00:18:00,560 well-established software development practices 514 00:18:00,560 --> 00:18:03,560 to provide that solid foundation for the LLM agent. 515 00:18:03,560 --> 00:18:04,480 Exactly. 516 00:18:04,480 --> 00:18:07,880 It's about offloading certain tasks or processes 517 00:18:07,880 --> 00:18:10,680 to components that we know are going to behave consistently 518 00:18:10,680 --> 00:18:11,720 and unpredictably. 519 00:18:11,720 --> 00:18:14,040 It's like building guardrails for the AI 520 00:18:14,040 --> 00:18:16,000 to ensure that those critical steps are always 521 00:18:16,000 --> 00:18:19,320 executed correctly, even if the LLM gets a little creative 522 00:18:19,320 --> 00:18:20,400 with its decision making. 523 00:18:20,400 --> 00:18:24,200 Yeah, it's about finding that right balance between AI autonomy 524 00:18:24,200 --> 00:18:26,360 and engineered reliability. 525 00:18:26,360 --> 00:18:27,520 So give us some examples. 526 00:18:27,520 --> 00:18:30,400 How does this integration actually work in practice? 527 00:18:30,400 --> 00:18:32,480 Well, think about context management. 528 00:18:32,480 --> 00:18:34,280 We can use traditional engineering 529 00:18:34,280 --> 00:18:36,120 to automatically trim and summarize 530 00:18:36,120 --> 00:18:38,720 information between those LLM calls. 531 00:18:38,720 --> 00:18:42,520 So the AI isn't overloaded with all that irrelevant data. 532 00:18:42,520 --> 00:18:45,120 It's like having an automated cleanup crew for the AI's 533 00:18:45,120 --> 00:18:49,360 mental workspace, just keeping things tidy and efficient. 534 00:18:49,360 --> 00:18:50,040 Exactly. 535 00:18:50,040 --> 00:18:52,040 Another example is tool management. 536 00:18:52,040 --> 00:18:54,720 So we can group similar tools together 537 00:18:54,720 --> 00:18:57,480 into these tool sets, creating this more organized 538 00:18:57,480 --> 00:18:59,200 and streamlined system. 539 00:18:59,200 --> 00:19:02,280 So instead of having 100 different tools just scattered 540 00:19:02,280 --> 00:19:05,720 about, we create these specialized kits 541 00:19:05,720 --> 00:19:07,120 for specific purposes. 542 00:19:07,120 --> 00:19:07,680 Exactly. 543 00:19:07,680 --> 00:19:10,600 It's like having a toolbox with clearly labeled compartments 544 00:19:10,600 --> 00:19:12,200 for all the different types of tools. 545 00:19:12,200 --> 00:19:14,200 So much easier to find the right tool for the job. 546 00:19:14,200 --> 00:19:14,920 Exactly. 547 00:19:14,920 --> 00:19:16,600 And we can even use traditional engineering 548 00:19:16,600 --> 00:19:18,520 to set up things like callbacks or triggers 549 00:19:18,520 --> 00:19:19,560 for certain events. 550 00:19:19,560 --> 00:19:20,320 Callbacks? 551 00:19:20,320 --> 00:19:23,200 We're talking about the AI returning our phone calls now. 552 00:19:23,200 --> 00:19:23,840 Not quite. 553 00:19:23,840 --> 00:19:26,320 It's more like setting up automated responses 554 00:19:26,320 --> 00:19:28,240 to specific situations. 555 00:19:28,240 --> 00:19:31,000 So for example, we might have a callback that automatically 556 00:19:31,000 --> 00:19:33,800 generates summary of a conversation when the LLM 557 00:19:33,800 --> 00:19:35,440 agents switches personas. 558 00:19:35,440 --> 00:19:38,440 So instead of relying on the AI to remember everything, 559 00:19:38,440 --> 00:19:41,560 we have a system in place that provides the relevant information 560 00:19:41,560 --> 00:19:42,320 automatically. 561 00:19:42,320 --> 00:19:42,880 Exactly. 562 00:19:42,880 --> 00:19:46,280 It's all about creating that more efficient and robust 563 00:19:46,280 --> 00:19:51,440 system by combining really the best of both worlds, AI 564 00:19:51,440 --> 00:19:52,920 and traditional engineering. 565 00:19:52,920 --> 00:19:53,440 I like it. 566 00:19:53,440 --> 00:19:54,560 It's a beautiful partnership. 567 00:19:54,560 --> 00:19:55,200 It is. 568 00:19:55,200 --> 00:19:56,960 And this integration also allows us 569 00:19:56,960 --> 00:19:59,760 to introduce the concept of short circuiting. 570 00:19:59,760 --> 00:20:01,040 OK, that sounds interesting. 571 00:20:01,040 --> 00:20:03,640 Is that like an AI safety feature, 572 00:20:03,640 --> 00:20:05,240 like a circuit breaker or something? 573 00:20:05,240 --> 00:20:07,160 It's more like an efficiency booster. 574 00:20:07,160 --> 00:20:07,560 OK. 575 00:20:07,560 --> 00:20:10,760 So sometimes the answer to a query is obvious, 576 00:20:10,760 --> 00:20:13,440 and the LLM agent doesn't need to go through this whole complex 577 00:20:13,440 --> 00:20:15,600 process to figure it out. 578 00:20:15,600 --> 00:20:17,520 So instead of overthinking it, the AI 579 00:20:17,520 --> 00:20:20,040 can just deliver a simple, straightforward answer. 580 00:20:20,040 --> 00:20:20,960 Exactly. 581 00:20:20,960 --> 00:20:21,400 Makes sense. 582 00:20:21,400 --> 00:20:22,800 Why waste the time and resources? 583 00:20:22,800 --> 00:20:23,560 Exactly. 584 00:20:23,560 --> 00:20:26,200 And that's a great example of how we can use traditional 585 00:20:26,200 --> 00:20:29,720 engineering to enhance the efficiency and performance 586 00:20:29,720 --> 00:20:31,440 of these LLM agents. 587 00:20:31,440 --> 00:20:33,000 It's like giving the AI permission 588 00:20:33,000 --> 00:20:34,800 to use its common sense. 589 00:20:34,800 --> 00:20:36,920 Avoid over-complicating things. 590 00:20:36,920 --> 00:20:38,040 You got it. 591 00:20:38,040 --> 00:20:40,000 And speaking of avoiding complications, 592 00:20:40,000 --> 00:20:41,600 there's another crucial consideration 593 00:20:41,600 --> 00:20:44,440 for building these real world LLM agents, 594 00:20:44,440 --> 00:20:47,480 and that is understanding their limitations. 595 00:20:47,480 --> 00:20:48,200 Right. 596 00:20:48,200 --> 00:20:51,600 Because as amazing as these systems are, they're not magic. 597 00:20:51,600 --> 00:20:53,800 They're things they can't do, at least not yet. 598 00:20:53,800 --> 00:20:54,120 Right. 599 00:20:54,120 --> 00:20:55,560 And the paper acknowledges this. 600 00:20:55,560 --> 00:20:58,560 It highlights a few key areas where more research 601 00:20:58,560 --> 00:21:00,000 and development are needed. 602 00:21:00,000 --> 00:21:00,920 So what are those? 603 00:21:00,920 --> 00:21:03,520 What are the biggest hurdles facing these LLM agents 604 00:21:03,520 --> 00:21:04,320 right now? 605 00:21:04,320 --> 00:21:07,000 Well, one area that's still pretty under explore 606 00:21:07,000 --> 00:21:10,040 is this idea of human in the loop evaluation. 607 00:21:10,040 --> 00:21:11,280 Humie in the loop. 608 00:21:11,280 --> 00:21:13,560 So instead of just running automated tests, 609 00:21:13,560 --> 00:21:16,520 we actually get real people involved in assessing 610 00:21:16,520 --> 00:21:17,760 the AI's performance. 611 00:21:17,760 --> 00:21:18,600 Exactly. 612 00:21:18,600 --> 00:21:21,800 It's about understanding how humans actually interact 613 00:21:21,800 --> 00:21:24,880 with these LLM agents, how they perceive their responses, 614 00:21:24,880 --> 00:21:27,960 and how we can improve that overall user experience. 615 00:21:27,960 --> 00:21:31,040 So it's like conducting those user studies or focus groups 616 00:21:31,040 --> 00:21:34,560 to get feedback on the AI's performance from a human perspective. 617 00:21:34,560 --> 00:21:35,160 Exactly. 618 00:21:35,160 --> 00:21:38,240 Because ultimately, these systems are designed to serve humans. 619 00:21:38,240 --> 00:21:40,520 So we really need to understand how people actually 620 00:21:40,520 --> 00:21:41,840 use them and what their needs are. 621 00:21:41,840 --> 00:21:42,480 Makes sense. 622 00:21:42,480 --> 00:21:44,600 So human in the loop evaluation is all 623 00:21:44,600 --> 00:21:48,440 about bridging that gap between AI and human interaction, 624 00:21:48,440 --> 00:21:52,120 ensuring that these systems are truly user friendly and effective 625 00:21:52,120 --> 00:21:53,720 in those real world scenarios. 626 00:21:53,720 --> 00:21:54,920 You got it. 627 00:21:54,920 --> 00:21:56,960 And then there's the question of model maintenance. 628 00:21:56,960 --> 00:21:57,720 Model maintenance. 629 00:21:57,720 --> 00:21:59,920 So are we talking about giving these AI's 630 00:21:59,920 --> 00:22:02,640 like regular oil changes and tune-ups? 631 00:22:02,640 --> 00:22:04,600 It's not quite that simple. 632 00:22:04,600 --> 00:22:07,960 It's more about understanding how to adapt and update 633 00:22:07,960 --> 00:22:10,480 these LLM agents as the world changes, 634 00:22:10,480 --> 00:22:13,760 as new information emerges, as user needs evolve. 635 00:22:13,760 --> 00:22:17,880 So it's like ongoing software updates, but for AI's. 636 00:22:17,880 --> 00:22:20,640 That's a good analogy, because unlike traditional software, 637 00:22:20,640 --> 00:22:22,960 LLMs are constantly learning and evolving. 638 00:22:22,960 --> 00:22:23,280 OK. 639 00:22:23,280 --> 00:22:25,360 So we need to develop these strategies for keeping 640 00:22:25,360 --> 00:22:26,840 them current and relevant. 641 00:22:26,840 --> 00:22:29,880 So it's like these AI systems need a continuous education 642 00:22:29,880 --> 00:22:32,800 program to keep up with the pace of change. 643 00:22:32,800 --> 00:22:33,360 Exactly. 644 00:22:33,360 --> 00:22:34,960 And that's a challenge we need to address 645 00:22:34,960 --> 00:22:37,240 if we want to build LLM agents that can truly 646 00:22:37,240 --> 00:22:38,600 stand the test of time. 647 00:22:38,600 --> 00:22:40,760 I can see how that would be a major undertaking, 648 00:22:40,760 --> 00:22:43,520 especially as these models become even more complex 649 00:22:43,520 --> 00:22:45,040 and integrated into our lives. 650 00:22:45,040 --> 00:22:45,640 It is. 651 00:22:45,640 --> 00:22:49,320 And it's an area where further research and innovation 652 00:22:49,320 --> 00:22:51,040 are definitely needed. 653 00:22:51,040 --> 00:22:53,240 But I believe it's a challenge worth tackling, 654 00:22:53,240 --> 00:22:56,720 because the potential benefits of these LLM agents 655 00:22:56,720 --> 00:22:58,440 are just enormous. 656 00:22:58,440 --> 00:22:58,960 Yeah. 657 00:22:58,960 --> 00:23:00,800 The possibilities are mind-boggling. 658 00:23:00,800 --> 00:23:03,760 Imagine a future where these systems are helping us 659 00:23:03,760 --> 00:23:07,360 with everything from managing our daily tasks 660 00:23:07,360 --> 00:23:09,480 to tackling some of the world's biggest problems. 661 00:23:09,480 --> 00:23:10,320 Exactly. 662 00:23:10,320 --> 00:23:11,760 It's an exciting prospect. 663 00:23:11,760 --> 00:23:14,720 And it's one that I'm personally very optimistic about. 664 00:23:14,720 --> 00:23:16,040 I share your optimism. 665 00:23:16,040 --> 00:23:19,160 I can't wait to see what the future holds for these LLM 666 00:23:19,160 --> 00:23:19,920 agents. 667 00:23:19,920 --> 00:23:21,840 But for now, let's take a little deeper 668 00:23:21,840 --> 00:23:23,800 look at some of the more practical considerations 669 00:23:23,800 --> 00:23:26,000 for building these incredible systems. 670 00:23:26,000 --> 00:23:26,880 OK, sounds good. 671 00:23:26,880 --> 00:23:30,000 And another big consideration is cost. 672 00:23:30,000 --> 00:23:31,080 Oh, right. 673 00:23:31,080 --> 00:23:33,120 All this AI power doesn't come cheap, does it? 674 00:23:33,120 --> 00:23:34,440 No, it definitely doesn't. 675 00:23:34,440 --> 00:23:35,720 We talked about model size. 676 00:23:35,720 --> 00:23:37,320 But there's so many other factors 677 00:23:37,320 --> 00:23:40,480 that can affect the cost of actually developing and deploying 678 00:23:40,480 --> 00:23:41,560 these LL agents. 679 00:23:41,560 --> 00:23:45,280 Like, give us a little glimpse into the economics 680 00:23:45,280 --> 00:23:47,160 of AI development. 681 00:23:47,160 --> 00:23:50,680 Well, for one, there's the choice between, 682 00:23:50,680 --> 00:23:52,880 do you use a pre-trained model, or do you 683 00:23:52,880 --> 00:23:54,440 try to fine-tune your own? 684 00:23:54,440 --> 00:23:56,840 So are we talking like, buy an off-the-shelf 685 00:23:56,840 --> 00:24:00,360 AI brain or build a custom one from scratch? 686 00:24:00,360 --> 00:24:02,480 That's a very simplified way to put it. 687 00:24:02,480 --> 00:24:02,960 OK. 688 00:24:02,960 --> 00:24:04,080 But yeah, you get the idea. 689 00:24:04,080 --> 00:24:06,800 And there are trade-offs, right, in terms of cost, performance, 690 00:24:06,800 --> 00:24:08,880 and just how much you can customize it. 691 00:24:08,880 --> 00:24:09,280 Right. 692 00:24:09,280 --> 00:24:12,680 So buying pre-trained is probably cheaper and faster. 693 00:24:12,680 --> 00:24:13,360 Right. 694 00:24:13,360 --> 00:24:16,440 But you might not get exactly what you need for your specific 695 00:24:16,440 --> 00:24:17,160 application. 696 00:24:17,160 --> 00:24:18,040 Exactly. 697 00:24:18,040 --> 00:24:19,840 And then there's the decision of, 698 00:24:19,840 --> 00:24:22,120 do you use open-source models, or do you 699 00:24:22,120 --> 00:24:24,520 go with the commercially available ones? 700 00:24:24,520 --> 00:24:27,480 Open-source, that means it's free to use and modify, right? 701 00:24:27,480 --> 00:24:27,960 Right. 702 00:24:27,960 --> 00:24:29,320 But commercial models, they probably 703 00:24:29,320 --> 00:24:31,880 come with, I don't know, better support, documentation, 704 00:24:31,880 --> 00:24:32,440 things like that. 705 00:24:32,440 --> 00:24:32,720 Yeah. 706 00:24:32,720 --> 00:24:33,920 You're totally getting it. 707 00:24:33,920 --> 00:24:36,600 It's like, are you going to build your furniture from scratch, 708 00:24:36,600 --> 00:24:39,680 or are you going to go to IKEA and get a ready-made set? 709 00:24:39,680 --> 00:24:40,240 Right. 710 00:24:40,240 --> 00:24:40,560 Right. 711 00:24:40,560 --> 00:24:41,200 Exactly. 712 00:24:41,200 --> 00:24:44,680 So it's this balancing act of cost versus convenience, 713 00:24:44,680 --> 00:24:46,280 customization versus support. 714 00:24:46,280 --> 00:24:47,320 Precisely. 715 00:24:47,320 --> 00:24:49,880 And all of those factors need to be carefully considered 716 00:24:49,880 --> 00:24:52,440 when you're building these real-world LLM agents. 717 00:24:52,440 --> 00:24:53,800 And then on top of that, I mean, 718 00:24:53,800 --> 00:24:56,240 we have to consider, can these LLM agents, 719 00:24:56,240 --> 00:24:59,680 can they ever truly achieve that human-level planning, 720 00:24:59,680 --> 00:25:01,520 a human-level decision-making? 721 00:25:01,520 --> 00:25:02,440 That's a big question. 722 00:25:02,440 --> 00:25:05,200 So are we saying AI will always be 723 00:25:05,200 --> 00:25:10,080 limited in its ability to plan and reason like we do? 724 00:25:10,080 --> 00:25:12,600 It's a question that's still very much open. 725 00:25:12,600 --> 00:25:15,480 The paper actually encourages more research in that area. 726 00:25:15,480 --> 00:25:18,120 How can we better understand the cognitive capabilities 727 00:25:18,120 --> 00:25:19,360 of these LLMs? 728 00:25:19,360 --> 00:25:22,760 And can they really achieve that more human-like intelligence? 729 00:25:22,760 --> 00:25:24,880 But even with all these complexities, 730 00:25:24,880 --> 00:25:27,160 there are some practical steps that we can take right now, 731 00:25:27,160 --> 00:25:30,720 today, to build those more robust and effective LLM agents. 732 00:25:30,720 --> 00:25:31,320 So what are they? 733 00:25:31,320 --> 00:25:32,840 Give us the inside scoop. 734 00:25:32,840 --> 00:25:35,000 One of the key takeaways from the paper 735 00:25:35,000 --> 00:25:38,000 is that we need to be very thoughtful about how we design 736 00:25:38,000 --> 00:25:39,200 and implement these systems. 737 00:25:39,200 --> 00:25:41,800 It's not just about, oh, let's throw a bunch of LLMs together 738 00:25:41,800 --> 00:25:42,720 and see what happens. 739 00:25:42,720 --> 00:25:42,920 Right. 740 00:25:42,920 --> 00:25:44,920 It's like any complex engineering project, right? 741 00:25:44,920 --> 00:25:47,920 You need a solid plan, the right tools, 742 00:25:47,920 --> 00:25:51,440 and an understanding of the challenges that you're facing. 743 00:25:51,440 --> 00:25:52,400 Exactly. 744 00:25:52,400 --> 00:25:55,200 And the paper actually highlights a few specific areas 745 00:25:55,200 --> 00:25:58,040 where we can make some pretty significant improvements. 746 00:25:58,040 --> 00:25:59,360 OK, so what are those areas? 747 00:25:59,360 --> 00:26:01,840 Give us the AI to-do list. 748 00:26:01,840 --> 00:26:04,880 One thing they really emphasize is output processing. 749 00:26:04,880 --> 00:26:06,320 Output processing, OK. 750 00:26:06,320 --> 00:26:09,360 So are we talking about proofreading the AI's writing 751 00:26:09,360 --> 00:26:10,400 assignments? 752 00:26:10,400 --> 00:26:11,360 Not exactly. 753 00:26:11,360 --> 00:26:14,040 It's more about making sure that the information that 754 00:26:14,040 --> 00:26:17,200 flows between those different LLM calls, 755 00:26:17,200 --> 00:26:21,560 it needs to be clear, concise, easily understood by the system. 756 00:26:21,560 --> 00:26:24,280 So it's like translating the AI's thoughts 757 00:26:24,280 --> 00:26:27,320 into a language that other AI's can understand. 758 00:26:27,320 --> 00:26:30,280 That's a really good way to put it, because humans, 759 00:26:30,280 --> 00:26:33,040 we might be able to decipher some messy language 760 00:26:33,040 --> 00:26:34,600 or ambiguous language. 761 00:26:34,600 --> 00:26:37,600 But AI's, they really need things to be structured 762 00:26:37,600 --> 00:26:38,600 and well-defined. 763 00:26:38,600 --> 00:26:40,600 Yeah, it's like the difference between speaking and slang 764 00:26:40,600 --> 00:26:42,440 and writing a formal report. 765 00:26:42,440 --> 00:26:46,440 The meaning might be the same, but that clarity, that precision, 766 00:26:46,440 --> 00:26:49,080 it's crucial for effective communication. 767 00:26:49,080 --> 00:26:49,880 Exactly. 768 00:26:49,880 --> 00:26:51,840 And so the paper actually suggests 769 00:26:51,840 --> 00:26:55,320 using those more structured formats, things like JSON 770 00:26:55,320 --> 00:26:58,120 or even executable code to make sure 771 00:26:58,120 --> 00:27:00,640 that that information is being transmitted accurately 772 00:27:00,640 --> 00:27:01,680 and efficiently. 773 00:27:01,680 --> 00:27:04,600 So we're not just letting the AI's chat amongst themselves 774 00:27:04,600 --> 00:27:06,800 in their own little quirky language. 775 00:27:06,800 --> 00:27:08,520 We're actually providing this framework 776 00:27:08,520 --> 00:27:10,000 for clear communication. 777 00:27:10,000 --> 00:27:10,920 You got it. 778 00:27:10,920 --> 00:27:13,440 And this focus on structured output, 779 00:27:13,440 --> 00:27:14,840 it also helps with error handling, 780 00:27:14,840 --> 00:27:17,720 which we talked about earlier, is just a major consideration 781 00:27:17,720 --> 00:27:19,160 when you're building these systems. 782 00:27:19,160 --> 00:27:20,000 Right, it makes sense. 783 00:27:20,000 --> 00:27:21,920 The more structured the information, the easier it is 784 00:27:21,920 --> 00:27:24,040 to spot and correct those errors. 785 00:27:24,040 --> 00:27:24,880 Exactly. 786 00:27:24,880 --> 00:27:27,800 It's like having a neatly organized spreadsheet 787 00:27:27,800 --> 00:27:30,960 versus just a jumbled pile of notes. 788 00:27:30,960 --> 00:27:35,160 It's much easier to find and fix those mistakes 789 00:27:35,160 --> 00:27:38,080 when things are laid out in a clear and logical way. 790 00:27:38,080 --> 00:27:39,840 Okay, so output processing is all 791 00:27:39,840 --> 00:27:43,400 about creating this more efficient and robust system 792 00:27:43,400 --> 00:27:46,200 by improving the way information flows 793 00:27:46,200 --> 00:27:47,440 between the different components. 794 00:27:47,440 --> 00:27:48,400 Precisely. 795 00:27:48,400 --> 00:27:50,520 And it's one of those practical steps 796 00:27:50,520 --> 00:27:52,280 that can really make a huge difference 797 00:27:52,280 --> 00:27:55,400 in the performance and reliability of these LLM agents. 798 00:27:55,400 --> 00:27:57,880 This is all super interesting stuff, 799 00:27:57,880 --> 00:28:00,240 but I have to admit, it's a lot to take in. 800 00:28:00,240 --> 00:28:01,160 Yeah. 801 00:28:01,160 --> 00:28:03,720 I feel like we're just scratching the surface here 802 00:28:03,720 --> 00:28:06,400 of what's possible with these agentic LLM systems. 803 00:28:06,400 --> 00:28:07,240 You're absolutely right. 804 00:28:07,240 --> 00:28:09,520 This paper is just a starting point. 805 00:28:09,520 --> 00:28:12,880 It's a glimpse into this vast and rapidly evolving world 806 00:28:12,880 --> 00:28:14,880 of AI agents. 807 00:28:14,880 --> 00:28:15,920 It's like we've opened this door 808 00:28:15,920 --> 00:28:18,560 to a whole new dimension of possibilities. 809 00:28:18,560 --> 00:28:21,520 Exactly, and I think one of the most exciting aspects 810 00:28:21,520 --> 00:28:24,000 is that these systems have the potential 811 00:28:24,000 --> 00:28:26,400 to completely transform the way we live and work. 812 00:28:26,400 --> 00:28:29,240 So we're not just talking about AI meal planners 813 00:28:29,240 --> 00:28:30,280 and virtual assistants. 814 00:28:30,280 --> 00:28:31,120 Right. 815 00:28:31,120 --> 00:28:33,160 We're talking about systems that can actually tackle 816 00:28:33,160 --> 00:28:34,720 some of the world's biggest challenges. 817 00:28:34,720 --> 00:28:35,560 Absolutely. 818 00:28:35,560 --> 00:28:38,680 Imagine AI agents that can help us address climate change, 819 00:28:38,680 --> 00:28:40,200 develop new medical treatments, 820 00:28:40,200 --> 00:28:42,880 maybe even explore the depths of space. 821 00:28:42,880 --> 00:28:45,120 Yeah, it's like we're giving these AI systems 822 00:28:45,120 --> 00:28:46,840 a seat at the table. 823 00:28:46,840 --> 00:28:49,240 We're inviting them to collaborate with us 824 00:28:49,240 --> 00:28:52,000 and solving some of humanity's most pressing problems. 825 00:28:52,000 --> 00:28:54,400 And that collaboration is going to require us 826 00:28:54,400 --> 00:28:56,200 to continue pushing those boundaries 827 00:28:56,200 --> 00:28:59,600 of what's possible with AI, to develop new techniques, 828 00:28:59,600 --> 00:29:04,400 and to very carefully consider the ethical implications 829 00:29:04,400 --> 00:29:06,560 of these incredibly powerful systems. 830 00:29:06,560 --> 00:29:08,760 It's a responsibility that we can't take lightly. 831 00:29:08,760 --> 00:29:09,600 Right. 832 00:29:09,600 --> 00:29:10,880 But it's also an incredible opportunity 833 00:29:10,880 --> 00:29:14,400 to shape the future in this positive and impactful way. 834 00:29:14,400 --> 00:29:15,680 I completely agree. 835 00:29:15,680 --> 00:29:17,880 This paper has given us a lot to think about. 836 00:29:17,880 --> 00:29:19,680 It's clear that the journey into the world 837 00:29:19,680 --> 00:29:23,160 of these agentic LLMs, it's just beginning. 838 00:29:23,160 --> 00:29:25,080 It's definitely an exciting time to be following 839 00:29:25,080 --> 00:29:26,640 the field of AI. 840 00:29:26,640 --> 00:29:28,920 And I can't wait to see what amazing developments are ahead. 841 00:29:28,920 --> 00:29:29,760 Yeah, me too. 842 00:29:29,760 --> 00:29:31,080 All right, so on that note, I think 843 00:29:31,080 --> 00:29:34,720 it's time to wrap up our deep dive into agentic LLMs. 844 00:29:34,720 --> 00:29:36,440 Thanks for joining us, listeners. 845 00:29:36,440 --> 00:29:39,480 And as always, we encourage you to kind of delve deeper 846 00:29:39,480 --> 00:29:42,520 into the research yourself, ask those questions, 847 00:29:42,520 --> 00:29:44,640 stay curious about the ever-evolving world 848 00:29:44,640 --> 00:29:46,280 of artificial intelligence. 849 00:29:46,280 --> 00:29:49,240 Until next time, keep learning, keep exploring, 850 00:29:49,240 --> 00:29:50,520 and keep diving deep. 851 00:29:50,520 --> 00:29:52,040 So before the break, we were talking 852 00:29:52,040 --> 00:29:55,600 about that incredible potential of these systems 853 00:29:55,600 --> 00:29:58,840 to really transform the way we live and work. 854 00:29:58,840 --> 00:29:59,600 Right, right. 855 00:29:59,600 --> 00:30:02,400 But as with any powerful technology, 856 00:30:02,400 --> 00:30:04,600 there are always those challenges and those limitations 857 00:30:04,600 --> 00:30:05,720 that we need to be aware of. 858 00:30:05,720 --> 00:30:06,360 Absolutely. 859 00:30:06,360 --> 00:30:09,440 And the paper, it doesn't shy away 860 00:30:09,440 --> 00:30:13,200 from acknowledging those areas where more research 861 00:30:13,200 --> 00:30:15,400 and development are really needed. 862 00:30:15,400 --> 00:30:18,120 And one of those areas, as we touched on a bit earlier, 863 00:30:18,120 --> 00:30:20,360 is this whole question of cost. 864 00:30:20,360 --> 00:30:22,760 Yeah, developing and deploying these AI systems, 865 00:30:22,760 --> 00:30:24,080 it's not cheap. 866 00:30:24,080 --> 00:30:25,480 It's a significant investment. 867 00:30:25,480 --> 00:30:26,120 It is. 868 00:30:26,120 --> 00:30:30,120 And it's not just about the computational resources 869 00:30:30,120 --> 00:30:33,520 to train and run these models, but also the expertise, 870 00:30:33,520 --> 00:30:35,880 the infrastructure, to build and maintain them. 871 00:30:35,880 --> 00:30:36,880 Yeah, exactly. 872 00:30:36,880 --> 00:30:38,560 And the paper really encourages us 873 00:30:38,560 --> 00:30:42,400 to think carefully about those economic trade-offs. 874 00:30:42,400 --> 00:30:44,040 When you're choosing different approaches 875 00:30:44,040 --> 00:30:47,040 to LLM agent development, for example, 876 00:30:47,040 --> 00:30:50,400 using a pre-trained model might be way more cost-effective 877 00:30:50,400 --> 00:30:53,000 than trying to build your own custom model from scratch. 878 00:30:53,000 --> 00:30:56,360 Right, it's like buying a ready-made car 879 00:30:56,360 --> 00:30:58,320 versus designing and building your own. 880 00:30:58,320 --> 00:30:59,360 Right, exactly. 881 00:30:59,360 --> 00:31:01,840 There are definitely some cost savings there 882 00:31:01,840 --> 00:31:05,720 to be had by going with that pre-built option. 883 00:31:05,720 --> 00:31:08,120 But you might have to compromise on certain features 884 00:31:08,120 --> 00:31:09,280 or customizations. 885 00:31:09,280 --> 00:31:10,680 That's a great analogy. 886 00:31:10,680 --> 00:31:12,600 And then there's also the decision of, 887 00:31:12,600 --> 00:31:15,120 do you use open-source models? 888 00:31:15,120 --> 00:31:18,960 Which are freely available, but might require a bit more 889 00:31:18,960 --> 00:31:21,880 technical expertise to implement, 890 00:31:21,880 --> 00:31:24,400 versus those commercially available models 891 00:31:24,400 --> 00:31:27,920 that often come with more support, more documentation, 892 00:31:27,920 --> 00:31:29,640 but they might be more expensive. 893 00:31:29,640 --> 00:31:32,360 So it's a balancing act, as with many things in life. 894 00:31:32,360 --> 00:31:33,280 It is. 895 00:31:33,280 --> 00:31:36,040 We need to weigh those costs, those benefits, 896 00:31:36,040 --> 00:31:37,760 of the different approaches. 897 00:31:37,760 --> 00:31:40,240 And choose the path that makes the most sense 898 00:31:40,240 --> 00:31:43,400 for our specific needs and resources. 899 00:31:43,400 --> 00:31:44,200 Precisely. 900 00:31:44,200 --> 00:31:46,720 And that kind of leads us to another really crucial 901 00:31:46,720 --> 00:31:48,600 consideration that they highlight in the paper. 902 00:31:48,600 --> 00:31:51,600 And that is this need for ongoing model maintenance. 903 00:31:51,600 --> 00:31:55,840 Yeah, I imagine keeping these LLM agents running 904 00:31:55,840 --> 00:32:00,680 smoothly and effectively is a bit like tending a garden. 905 00:32:00,680 --> 00:32:04,360 It requires that constant care and attention and adjustments 906 00:32:04,360 --> 00:32:06,240 to make sure that everything's thriving. 907 00:32:06,240 --> 00:32:07,800 That's a great way to think about it. 908 00:32:07,800 --> 00:32:08,640 Yeah. 909 00:32:08,640 --> 00:32:10,520 Yeah, we need to be prepared to adapt 910 00:32:10,520 --> 00:32:12,440 and update these models, you know? 911 00:32:12,440 --> 00:32:15,560 As the world changes, as new information emerges, 912 00:32:15,560 --> 00:32:17,840 as user needs evolve. 913 00:32:17,840 --> 00:32:20,160 It's almost like providing these AI systems 914 00:32:20,160 --> 00:32:22,480 with a continuous education program, 915 00:32:22,480 --> 00:32:24,120 constantly feeding them new knowledge, 916 00:32:24,120 --> 00:32:27,080 refining their skills to keep them sharp and relevant. 917 00:32:27,080 --> 00:32:27,920 Exactly. 918 00:32:27,920 --> 00:32:29,440 And that ongoing maintenance, it's 919 00:32:29,440 --> 00:32:32,360 essential to ensure that those LLM agents remain 920 00:32:32,360 --> 00:32:34,520 useful and reliable over time. 921 00:32:34,520 --> 00:32:37,560 So it's not a set it and forget it kind of technology. 922 00:32:37,560 --> 00:32:40,280 It requires a commitment to that ongoing investment, 923 00:32:40,280 --> 00:32:44,440 that adaptation to really keep pace with that ever-changing 924 00:32:44,440 --> 00:32:46,320 landscape of information and technology. 925 00:32:46,320 --> 00:32:47,120 Absolutely. 926 00:32:47,120 --> 00:32:50,040 And that brings us to another key takeaway from the paper, 927 00:32:50,040 --> 00:32:51,000 I think. 928 00:32:51,000 --> 00:32:53,880 And that is that need to recognize and address 929 00:32:53,880 --> 00:32:57,480 the limitations of current LLM technology. 930 00:32:57,480 --> 00:33:01,160 Right, because as impressive as these systems are, 931 00:33:01,160 --> 00:33:02,560 they're not a silver bullet, right? 932 00:33:02,560 --> 00:33:03,060 Right. 933 00:33:03,060 --> 00:33:05,280 And certain things they just can't do, at least not yet. 934 00:33:05,280 --> 00:33:05,960 Exactly. 935 00:33:05,960 --> 00:33:08,240 And the paper really encourages us 936 00:33:08,240 --> 00:33:12,040 to be realistic about the capabilities of LLMs 937 00:33:12,040 --> 00:33:15,640 and to avoid overhyping their potential. 938 00:33:15,640 --> 00:33:18,120 We're still very much in the early stages 939 00:33:18,120 --> 00:33:21,000 of developing truly agentic systems. 940 00:33:21,000 --> 00:33:23,760 And there are lots of challenges that remain to be addressed. 941 00:33:23,760 --> 00:33:28,480 So it's a journey of continuous discovery and improvement, 942 00:33:28,480 --> 00:33:30,280 not a destination we've already reached. 943 00:33:30,280 --> 00:33:31,520 That's a great way to put it. 944 00:33:31,520 --> 00:33:34,800 And this journey, it really requires a collaborative effort 945 00:33:34,800 --> 00:33:38,920 between the researchers, the developers, and also the users 946 00:33:38,920 --> 00:33:41,240 to push those boundaries of what's possible, 947 00:33:41,240 --> 00:33:44,360 but also to be mindful of the ethical implications 948 00:33:44,360 --> 00:33:46,280 of these really powerful technologies. 949 00:33:46,280 --> 00:33:47,240 It's a balancing act. 950 00:33:47,240 --> 00:33:47,740 It is. 951 00:33:47,740 --> 00:33:51,520 Between ambition and responsibility, 952 00:33:51,520 --> 00:33:54,600 between the excitement of exploring these new frontiers, 953 00:33:54,600 --> 00:33:57,800 and the need to proceed with caution and awareness. 954 00:33:57,800 --> 00:33:58,440 Exactly. 955 00:33:58,440 --> 00:34:00,000 And I think that balanced approach, 956 00:34:00,000 --> 00:34:03,640 it's really essential to ensure that these LLM agents ultimately 957 00:34:03,640 --> 00:34:07,640 benefit humanity and contribute to a brighter future. 958 00:34:07,640 --> 00:34:08,800 Yeah, for sure. 959 00:34:08,800 --> 00:34:11,040 This paper has given us a lot to think about. 960 00:34:11,040 --> 00:34:11,760 It really has. 961 00:34:11,760 --> 00:34:14,960 And it's clear that the world of agentic LLMs, 962 00:34:14,960 --> 00:34:17,680 it's just full of possibilities and challenges. 963 00:34:17,680 --> 00:34:19,360 It's been a fascinating deep dive. 964 00:34:19,360 --> 00:34:20,120 It has. 965 00:34:20,120 --> 00:34:23,240 And I'm sure our listeners are eager to learn even more 966 00:34:23,240 --> 00:34:25,360 about this rapidly evolving field. 967 00:34:25,360 --> 00:34:26,020 I agree. 968 00:34:26,020 --> 00:34:28,800 And I encourage everyone to stay curious, 969 00:34:28,800 --> 00:34:32,240 explore the research, engage in those thoughtful discussions 970 00:34:32,240 --> 00:34:35,400 about the potential and the implications of these LLM 971 00:34:35,400 --> 00:34:35,960 agents. 972 00:34:35,960 --> 00:34:36,560 Absolutely. 973 00:34:36,560 --> 00:34:38,360 Thanks for joining us on this exploration. 974 00:34:38,360 --> 00:34:41,440 And until next time, keep learning, keep questioning, 975 00:34:41,440 --> 00:34:43,320 and keep diving deep into the world 976 00:34:43,320 --> 00:34:59,800 of artificial intelligence.