The best researchers from Yale, Stanford, Google DeepMind, and Microsoft laid out all we know about Agents in a 264-page paper [book],
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The best researchers from Yale, Stanford, Google DeepMind, and Microsoft laid out all we know about Agents in a 264-page paper [book],
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Here are some of their key findings:
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Here are some of their key findings:
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They build a mapping of different agent components, such as perception, memory, and world modelling, to different regions of the human brain and compare them:
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They build a mapping of different agent components, such as perception, memory, and world modelling, to different regions of the human brain and compare them:
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- brain is much more energy-efficient
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- brain is much more energy-efficient
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- no genuine experience in agents
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- no genuine experience in agents
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- brain learns continuously, agent is static
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- brain learns continuously, agent is static
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An agent is broken down to:
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An agent is broken down to:
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- Perception: the agent's input mechanism. can be improved with multi-modality, feedback mechanisms (e.g., human corrections), etc.
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- Perception: the agent's input mechanism. can be improved with multi-modality, feedback mechanisms (e.g., human corrections), etc.
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- Cognition: learning, reasoning, planning, memory. LLMs are key in this part. 
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- Cognition: learning, reasoning, planning, memory. LLMs are key in this part. 
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- Action: agent's output and tool use.
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- Action: agent's output and tool use.
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Agentic memory is represented as:
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Agentic memory is represented as:
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- Sensory memory or short-term holding of inputs which is not emphasized much in agents. 
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- Sensory memory or short-term holding of inputs which is not emphasized much in agents. 
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- Short-term memory which is the LLM context window 
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- Short-term memory which is the LLM context window 
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- Long-term memory which is the external storage such as RAG or knowledge graphs.
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- Long-term memory which is the external storage such as RAG or knowledge graphs.
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The memory in agents can be improved and researched in terms of:
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The memory in agents can be improved and researched in terms of:
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- increasing the amount of stored information
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- increasing the amount of stored information
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- how to retrieve the most relevant info
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- how to retrieve the most relevant info
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- combining context-window memory with external memory
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- combining context-window memory with external memory
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- deciding what to forget or update in memory
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- deciding what to forget or update in memory
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The agent must simulate or predict the future states of the environment for planning and decision-making.
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The agent must simulate or predict the future states of the environment for planning and decision-making.
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ai world models are much simpler than the humans' with their causal reasoning (cause-and-effect) or physical intuition. 
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ai world models are much simpler than the humans' with their causal reasoning (cause-and-effect) or physical intuition. 
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LLM world models are mostly implicit and embedded.
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LLM world models are mostly implicit and embedded.
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EMOTIONS are a deep aspect of humans, helping them with social interactions, decision-making, or learning. 
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EMOTIONS are a deep aspect of humans, helping them with social interactions, decision-making, or learning. 
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Agents must understand emotions to better interact with us. 
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Agents must understand emotions to better interact with us. 
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But rather than encoding the feeling of emotions, they have a surface-level modelling of emotions.
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But rather than encoding the feeling of emotions, they have a surface-level modelling of emotions.
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Perception is the process by which an agent receives and interprets raw data from its surroundings.
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Perception is the process by which an agent receives and interprets raw data from its surroundings.
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READ PAPER: 
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READ PAPER: 
Advances and Challenges in Foundation Agents: From Brain-Inspired
  Intelligence to Evolutionary, Collaborative, and Safe Systems (2504.01990)
https://huggingface.co/papers/2504.01990
2504.01990
paper
resource
https://huggingface.co/papers/2504.01990
The best researchers from Yale, Stanford, Google DeepMind, and Microsoft laid out all we know about Agents in a 264-page paper [book],

Here are some of their key findings:

They build a mapping of different agent components, such as perception, memory, and world modelling, to different regions of the human brain and compare them:

- brain is much more energy-efficient
- no genuine experience in agents
- brain learns continuously, agent is static

An agent is broken down to:
- Perception: the agent's input mechanism. can be improved with multi-modality, feedback mechanisms (e.g., human corrections), etc.
- Cognition: learning, reasoning, planning, memory. LLMs are key in this part. 
- Action: agent's output and tool use.

Agentic memory is represented as:
- Sensory memory or short-term holding of inputs which is not emphasized much in agents. 
- Short-term memory which is the LLM context window 
- Long-term memory which is the external storage such as RAG or knowledge graphs.

The memory in agents can be improved and researched in terms of:
- increasing the amount of stored information
- how to retrieve the most relevant info
- combining context-window memory with external memory
- deciding what to forget or update in memory

The agent must simulate or predict the future states of the environment for planning and decision-making.

ai world models are much simpler than the humans' with their causal reasoning (cause-and-effect) or physical intuition. 

LLM world models are mostly implicit and embedded.

EMOTIONS are a deep aspect of humans, helping them with social interactions, decision-making, or learning. 

Agents must understand emotions to better interact with us. 

But rather than encoding the feeling of emotions, they have a surface-level modelling of emotions.

Perception is the process by which an agent receives and interprets raw data from its surroundings.

READ PAPER: https://huggingface.co/papers/2504.01990
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