Introduction
As we witness the rapid evolution of artificial intelligence (AI), Large Language Models (LLMs) and generative AI are significantly redefining our technological landscape. These sophisticated models, including OpenAI’s renowned ChatGPT and DALL-E, have transformed numerous areas, from search engines to content generation and code writing. With robust backing from industry giants like Microsoft, they’re paving the way towards an innovative ‘AI-topia’.
In this exploration, we will focus on ‘smart agents’—digital entities that leverage the power of LLMs to communicate with users and execute tasks on their behalf. Essentially, smart agents act as our digital intermediaries, taking our prompts and executing corresponding tasks. This article provides an in-depth examination of smart agents, their unique characteristics, their relevance in the current AI sphere, and the libraries that support their development. We will also offer a sneak peek into a smart agent infrastructure demo developed by our team.
What are smart agents?
Smart agents, in essence, are like digital task forces that comprehend and carry out tasks through a chat-based interface, using Python as their primary programming language. These agents are distinguished from conventional bots by their integration with LLMs, which allows them to understand text and commands, enabling a broad spectrum of functionality.
Smart agents are versatile tools in the digital realm. Their capabilities range from ordering food to planning vacations, performing complex calculations, or writing sophisticated computer code.
What are they made of?
Smart agents consist of several key components:
- LLM Models: These serve as the brain of the smart agent, facilitating its intelligent functionality.
- Prompts: These bridge the gap between the user, the agent, and the LLM. They are vital for successful interactions and task execution, as they provide the tasks to the LLM in a language it understands.
- Memory: This module stores the prompts and the LLM responses for future reference. The memory can be divided into short-term and long-term segments, reflecting the inherent structure of the LLM.
- Chains: For complex tasks, chains can be used to link multiple LLMs or other modules together to perform a sequence of tasks.
- Interfaces: These serve as digital gateways that connect the agent to external resources, allowing it to pull information from the internet, access email accounts, make bookings, and more.
How to create a Smart Agent
The process of creating a smart agent is complex and requires a robust infrastructure followed by the integration of the ‘intelligence’ or ‘brains’ of the system. Our team specializes in building smart agents for a variety of applications, ranging from entertainment to finance, coding, and more. We can also train your LLM agent to improve its performance over time.
In the following short video, we provide a preview of a project that showcases how to create character-specific interactions in a fun and engaging manner.
If you prefer a hands-on approach and wish to build your own platform, there are several leading frameworks available:
- LangChain: This framework provides the basic components needed to build smart agents powered by language models.
- Haystack: An open-source Python framework suitable for developing powerful search systems for handling large document collections.
- Hugging Face Transformer Agent: This is an interpreter for natural language APIs built on transformers. It is also extensible, allowing for the addition of any tool developed by the community.
In conclusion, smart agents are a rapidly emerging facet of LLMs and should be considered for integration into future applications. If you are interested in learning more about LLMs, Generative AI, and Smart Agents, please don’t hesitate to contact me at avidor@ioteratech.com. I look forward to hearing from you.