From: redpointai
AI agents, also known as Large Action Models (LAMs), represent a significant frontier in artificial intelligence, moving beyond mere language generation to enabling models to take actions and interact with the world [00:12:47]. David Luan, Head of the AGF Lab at Amazon and former co-founder of Adept, highlights the challenges and opportunities in this space [00:00:19].
Current State and Challenges of AI Agents
Early powerful Large Language Models (LLMs) like GPT-4, while capable of tasks like generating rap songs or performing three-digit addition, struggled with real-world actions such as ordering a pizza [00:11:53]. This highlighted a major gap in their utility [00:14:14]. The shift to agents involves enabling LLMs to use tools and decide when to perform actions [00:13:31].
A primary challenge for agents is reliability [00:14:20]. Early models, primarily behavioral cloners, tend to “go off the rails” when encountering unforeseen situations, leading to unpredictable behavior [00:13:47]. For enterprise adoption, such as invoice processing, agents must achieve near-perfect reliability, as even a small error rate (e.g., deleting a third of QuickBooks entries one in seven times) renders them unusable [00:14:44].
Currently, systems like “Operator” show impressive capabilities, but their end-to-end reliability for complex tasks is still very low, requiring frequent human intervention [00:15:01]. The goal is for businesses to trust agents in a “fire and forget” manner [00:15:29].
Advancements in Agent Development
To transform a base multimodal model into a Large Action Model (LAM), two main problems must be solved:
- Engineering Problem: Exposing what the model can do in a “model-legible” way, including APIs, UI elements, and teaching it about specific applications like Expedia or SAP [00:15:46].
- Research Problem: Teaching the model to plan, reason, replan, follow user instructions, and even infer user intent [00:16:18]. This multi-step decision-making process involves backtracking, predicting action consequences, and understanding potential dangers (e.g., a “delete button”) [00:17:00].
This development aligns with a progression seen in AI model training: pre-training (exposition), supervised fine-tuning (sample problems), and Reinforcement Learning (RL) (open-ended problems with answers in the back) [00:17:34].
The Role of Reinforcement Learning (RL)
A key insight for advancing AI technology is that LLMs, by design, are penalized for discovering new knowledge because it wasn’t part of their training data [00:05:20]. To overcome this, combining LLMs with RL and search paradigms is crucial [00:05:44]. RL, demonstrated by successes like AlphaGo, enables models to discover new knowledge [00:05:31]. This integration allows systems to leverage existing human knowledge while also building upon it [00:05:48].
Models are also better at verifying their own work than generating correct answers, and RL exploits this by forcing models to repeatedly try to satisfy their internal sense of correctness [00:08:05].
Engineering as a Bottleneck
The development of advanced AI models and agents is shifting from “alchemy to industrialization” [00:09:16]. A modern AI lab’s job is to build a “factory that reliably turns out models,” which requires significant investment in repeatability and infrastructure [00:08:54]. Key engineering challenges include:
- Managing massive computing clusters reliably over long periods, ensuring that job progress isn’t lost if a node fails [00:09:50].
- Developing systems where data centers perform local inference, learn from new customer environments, and send new knowledge back to a centralized model for continuous improvement [00:10:01].
Implications and Future Outlook
Generalization and Original Thinking
Models are capable of generalizing more broadly than often assumed [00:06:45]. Improvements in verifiable domains (like coding and math) can transfer to fuzzier problems (like healthcare or law) [00:07:15]. Examples from Open AI’s early work with RL in flash games showed models discovering novel “speedrun techniques” (e.g., glitching through walls) that humans hadn’t performed before, demonstrating original thinking [00:11:10].
Future of AI Agents in Productivity Tools
The main milestone for agents is achieving 100% task completion during training [00:21:03]. This is analogous to the self-driving car problem, where impressive demos existed years ago, but full reliability is still elusive [00:20:21]. The belief is that with the right tools and training recipes, agents can achieve a level of reliability similar to “Level 4” self-driving, where they can execute any task perfectly after sufficient training time [00:20:41].
Human-Computer Interaction
Current human-AI interfaces, primarily chat-based, are “low bandwidth” and limit what can be achieved, akin to early, simplistic iPhone apps [00:18:50]. Future interfaces should be more dynamic and multimodal, with agents synthesizing custom UIs to best elicit information from users and foster shared context between human and AI [00:19:38]. The goal is to increase the “leverage per unit energy a human spends with computing” [00:25:10]. This includes ambient computing and other new tools, alongside existing interfaces like command lines and GUIs [00:24:57].
Enterprise Adoption of AI Agents and Market Dynamics
While there is some uncertainty, it’s predicted that AGI (Artificial General Intelligence) (defined as a model capable of doing anything useful a human does on a computer and learning as fast as a generalist human) is “really not super far away” [00:22:03]. However, the diffusion of this technology into society will likely lag behind the technical capability, creating a “capability overhang” [00:22:57]. The bottleneck for adoption will be “people and processes,” including social acceptance and figuring out how to co-design interfaces with human users [00:23:10]. This creates a significant opportunity for startups to bridge the gap between advanced model capabilities and end-user needs [00:23:49].
Specialized vs. Generalist Models
Specialized models will exist, not primarily for technical reasons, but due to policy considerations, such as companies not wanting their data comingled or divisions within a company requiring information barriers (e.g., sales and trading vs. investment banking at a bank) [00:25:45].
AI Robotics and World Models
Digital agents offer an opportunity to de-risk hard problems in physical agents by solving reliability in a simulated or digital space first, before engaging with costly real-world deployments [00:34:11]. If 100% task reliability can be achieved in the digital space, it will likely transfer to the physical space [00:35:32].
Another major open problem is developing “world models” [00:36:04]. While RL can work where explicit verifiers or simulators exist (e.g., theorem proving, staging environments for apps), world modeling is the answer for problems without such explicit feedback mechanisms [00:36:46]. This also relates to video models and their understanding of physics for open-ended exploration [00:36:02].
State and Future of AI Agents
The overall effectiveness of AI agents is rapidly improving, with model progress this year expected to be even greater than last year, despite appearing similar on the surface [00:42:43]. Underhyped aspects include solving extremely large-scale simulation for models to learn from [00:43:07]. The belief is that the remaining open problems are solvable without needing fundamentally new computational paradigms like quantum computers or replacing gradient descent [00:26:54].
David Luan’s experience at OpenAI highlighted the importance of team culture – hiring intrinsically motivated, intellectually flexible individuals, often earlier in their careers [00:32:08]. This approach fosters adaptation as the “optimal playbook changes” every few years [00:32:37]. He also notes that technical differentiation in AI models doesn’t always compound as expected; breakthroughs in one area (e.g., text modeling) don’t deterministically guarantee leadership in subsequent areas (e.g., multimodal, reasoning, agents) [00:32:51].