Himanshu Ramchandani
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Agents are Not Enough [Paper Unfold]

Agents are Not Enough [Paper Unfold]

Unfolding the Research Paper - Agents are Not Enough

Himanshu Ramchandani
January 11, 2025 • Estimated Reading Time: 4 minutes

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Paper Unfold is a series in which you will get a breakdown of complex research papers into easy-to-understand pointers.

Research Paper

Last month another paper was published.

The current state of AI agents is not up to the mark.

In the paper past agent failures, limitations in generalization, scalability, coordination, robustness, and ethical considerations are highlighted.

They propose a new ecosystem incorporating

Agents (task-specific modules)
Sims (user representations)
Assistants (user-facing interfaces coordinating Agents and Sims)

Let’s dive in!

Agents are programs that work independently to complete tasks for humans.

They are autonomous, programmable, responsive to changes, and proactive.

Agentic AI focuses on systems that make decisions independently without human intervention.

You can learn more about them in Google’s white paper on AI Agents.

Key Components

Ecosystem demonstrated in the Paper

Agents
are focused tools designed to perform specific tasks.
Sims
represent a user by capturing their preferences, behaviors, and profiles.
Assistants
interact directly with users and can call on Sims and Agents to complete tasks.

The interaction between Agents, Sims, and Assistants is designed to be highly collaborative.

History of Agents

Early AI agents from the 1950s used symbolic reasoning but couldn't handle real-world complexity.

Expert systems in the 1980s worked well for specific tasks but failed in broader applications because they required too much manual setup.

Reactive agents in the 1990s could respond to their environment but couldn't plan or learn from experience.

Cognitive architectures tried to mimic human thought processes but weren't fast enough or scalable for real-time use.

Multi-Agent Systems

Multi-agent systems, where several agents work together, showed potential for solving problems but struggled with communication, coordination, and scaling.

Limitations and Challenges

Most AI agents are designed for specific tasks and can't adapt to new situations or domains.
Many AI agents are fragile—they work well in ideal conditions but fail when something unexpected happens.
As tasks become more complex, the computational power needed for agents increases rapidly.
Agents that cannot adapt to the user’s needs or context may be of limited use.
Users must trust agents, especially when the tasks involve sensitive information.