# Why AI Agents Don't Actually Understand You — Danielle Perszyk, Amazon AGI Lab

## Executive summary

The discussion explores the next frontier of Artificial General Intelligence (AGI), arguing that current AI models are fundamentally limited by their focus on narrow tasks (like chatbots or coding agents). True AGI must emulate human intelligence, which is inherently collective and social. The core technical shift required involves building 'perception agents' capable of real-time interaction, possessing sophisticated world models, and achieving alignment by modeling the user's intent and preferences rather than just automating clicks.

## Key takeaways

- Human Intelligence is Collective: The speaker emphasizes that human intelligence is fundamentally social; it emerges from interactions, diversity, and interconnectivity (the 'collective brain'). AI must be built to extend these collective processes for all users, not just engineers.
- Shift from Automation to Intent Modeling: The ultimate goal of perception agents is not merely reliable clicking or scrolling (RPA), but decomposing a high-level human intention and executing it, much like an executive assistant understands the user's mind and preferences.
- Alignment as the Core Objective: The most foundational scientific goal for AGI is optimizing for 'aligning representations'—the mechanism by which humans generalize knowledge. This shifts the focus from merely predicting the next token or solving specific tasks to achieving generalized cognitive alignment.

## Technical details

- Perception Agents & World Models: Amazon AGI Lab is developing agents that must perceive the digital environment in the same way humans do, requiring not only understanding of the digital space but also robust 'world models' based on physical reality. This enables real-time interaction and context updating.
- Real-Time Interaction: Unlike current batch processing, true AGI requires agents to constantly update understanding in real time, negotiating meaning while interacting—a paradigm shift from the 'local attractor state of chatbots and coding agents.'
- Memory Architecture: The discussion suggests that memory is not just storage. AGI needs multiple types of memory, including episodic memories, which are crucial for individual perspectives and efficient information retrieval.
- Computational Level (Marr's Levels): The speaker advocates that research must focus on the 'computational level'—the goal the AI is trying to achieve—rather than just the algorithmic or implementation levels, arguing this is key for generalization and augmentation.

## Practical implications

- Future AI systems must move beyond simple automation (RPA) to model complex human intent and preferences.
- The focus for build engineers should shift from optimizing for specific tasks or metrics (which can be 'reward hacked') to building generalized cognitive architectures that prioritize alignment with human representations.
- Developing multi-agent systems requires designing for emergent, fluid collaboration rather than structured role delegation.

## Topics

Artificial General Intelligence (AGI), Cognitive Science, Machine Learning Architectures, Multi-Agent Systems, Computational Theory, Amazon AGI Lab, Adept, Nova Act

Source: https://www.youtube.com/watch?v=K796MYUgt0k