AI Agents

2024-12-12 | Mick Kalle Mickelborg

2025

AI agents will be huge in 2025. AI Agents have the capability to break the boundaries of conventional language models beyond text generation into the realm of intelligent agency, decisionmaking and function calling. The amount of resources available is growing, and the barrier of entry is diminishing every day.

Resources for getting started

The course (offered free of charge, by the way) by Dawn Song at UC Berkeley delves into hands-on approaches to building AI Agents from scratch. The curriculum encompasses foundational LLM concepts, reasoning, planning, tool utilization, and infrastructure development for agent deployment. It also explores specific applications such as code generation, robotics, web automation, medical fields, and scientific discovery. The course features lectures from esteemed professionals, including Denny Zhou from Google DeepMind and Shunyu Yao from OpenAI, making the course that much more of a no-brainer to anyone interested in a comprehensive understanding of LLM agents' capabilities and challenges.

What I really appreciated about this course is that, although it also covered some theoretical aspects, it was deeply hands on, particularly with:

• Tool integration: Using APIs for live data, automating web interactions, or enabling physical actions in robotics.
• Application domains: Examples include automating coding tasks, synthesizing scientific research, supporting healthcare decisions, and navigating the web to retrieve and analyze information.
• Infrastructure: Setting up robust systems for deploying LLM agents at scale, ensuring reliability and effective error handling.

Use Cases

The practical potential of LLMs is vast. For example, in code generation, agents can autonomously write, debug, and deploy programs. In scientific discovery, they assist in data analysis and hypothesis testing. In healthcare, they support diagnostics and decision-making. In robotics, they help physical systems act on high-level language instructions.

However, the course also highlights challenges. AI Safety has been an area of interest for mine for over a year, and particularly mechanistic interpretability. Hallucinations, where models confidently generate incorrect information, remain a significant issue. Ethical concerns around privacy, bias, and potential misuse were critically examined, along with strategies for mitigating these risks.

Limitations

A key takeaway is that while current LLM agents are powerful, they are not infallible. Addressing their limitations will require advances in reasoning capabilities, error correction, and ethical design.

The course offered a comprehensive understanding of LLM agents, combining technical depth with practical insights. It left me inspired to contribute to shaping the future of AI responsibly.