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Decoding Data & AI: Moving from Language Models to actual AI agents
Decoding Data & AI: Moving from Language Models to actual AI agents
Understanding the evolution from language models to intelligent AI agents
In our Decoding Data & AI (Artificial Intelligence) series, we provide you with key insights for successful data & AI projects to boost your business. Part 9 of our series delves into AI agents: by now, you are probably quite familiar with conversational AI chatbots like ChatGPT, offering you information and the production of content (text, code, pictures, etc.) based on your input. But wouldn't it be nice if your AI assistant could actually do tasks for you, i.e. after being prompted, take some action on your behalf, including the decision of what exactly to do?
In the rapidly evolving landscape of artificial intelligence, this seems to be the next big step: AI agents are emerging as powerful tools for businesses.
AI agents
Very fundamentally, An AI agent is a system that perceives its environment and takes actions to achieve specific goals. Unlike traditional programmatic systems, where outputs for given inputs are explicitly defined in the code (if you receive signal X, do Y), AI agents use AI capabilities to produce relevant outputs based on the entire context they receive. This flexibility allows AI agents to handle complex, unpredictable situations and adapt to changing environments - a crucial capability in today's fast-paced business world. An AI agent receives a goal from you, can independently scan the relevant context, and take a decision accordingly.
From individual models to complex systems
Building upon the foundation of AI agents, Large Action Models (LAMs) represent the next step in the AI evolution. LAMs differ from Large Language Models (LLMs) in their ability to not just process and generate text, but to take concrete actions in the digital world. Essentially, a LAM is an enhanced version of an LLM, augmented with tools and connectors to various other systems. This enables the AI to interact with databases, scan entire websites, read and write emails, send text messages, create and upload visual content, and perform a wide range of other tasks. A prime example of a first LAM implementation is the Rabbit R1, a pocket-sized AI device that can understand and execute complex user commands across various applications and services, without a user interface - the way to prompt it is purely by talking to it.
For companies looking to build their own AI agents and LAMs, several development frameworks and platforms can facilitate the process. Langchain, CrewAI, and AutoGen are some of the leading tools for constructing these advanced agentic systems. Those frameworks allow developers to build sophisticated AI workflows by connecting LLMs with various data sources and APIs, making it easier to create robust, multi-step processes which don’t require human intervention.
Additionally, LLM platforms themselves, such as OpenAI’s function-calling capabilities (e.g., OpenAI’s function calling and Llama’s function API), offer native support for building agents that can perform real-world tasks, making the development of AI agents more accessible to businesses of all sizes.
The future of AI models
AI agents and LAMs hold immense potential for automating complex workflows and enhancing efficiency across various industries. However, it’s essential to note that while these agents are powerful, their outputs are often volatile and not easily reproducible, meaning businesses should adopt them with caution. Yet, the pace of AI advancement remains staggering. As the technology matures, AI agents are poised to become ubiquitous tools that will help businesses across the globe turbo-charge their efficiency, unlocking new opportunities for innovation and growth.
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