AI-Powered Transformation (4th Episode) - AI Agents Framework

In our previous article, we delved into the complexities of the Retrieval-Augmented Generation (RAG) concept and how Adam, our business analyst at a leading bank, leveraged this advanced AI technique to enhance various business processes. Now, Adam is ready to delve into another fascinating area of AI: AI Agent Frameworks. These frameworks enable Large Language Models (LLMs) to perform a wide range of tasks more effectively by integrating specialized tools and functions.

If you didn't check the last episodes from the AI-Powered Transformation Articles, You can check them using the links below:

• AI-Powered Transformation for Business Analysts and Architects (1st Episode) - A Comprehensive Guide
• AI-Powered Transformation (2nd Episode) - Talking to LLMs
• AI-Powered Transformation (3rd Episode) - Guided By Data

About The Authors

1. The Challenge with LLMs

Large Language Models (LLMs) trained to perform causal language modeling can tackle many tasks, but they often need help with essential functions like logic, calculation, and search. When prompted in domains where they do not perform well, they usually fail to generate the answer we expect.

2. What is an AI Agent?

An AI agent is a system that uses an LLM as its engine and has access to functions called tools. These tools are functions for performing specific tasks and contain all necessary descriptions for the agent to use them properly. The agent can be programmed to:

• Devise a Series of Actions/Tools and Run Them All at Once: For example, a CodeAgent can execute a series of code snippets to achieve a specific goal.
• Plan and Execute Actions/Tools One by One: The agent waits for the outcome of each action before launching the next one, allowing for more dynamic and adaptive task execution.

3. Key Components of an AI Agent Framework

• LLM Engine: The agent's core is the LLM, which generates responses based on input. The LLM is responsible for understanding the context and devising a plan to achieve the desired outcome.
• Tools: Tools are specialized functions the agent can call upon to perform specific tasks. These tools are designed to handle tasks the LLM may struggle with, such as calculations, logic operations, or data retrieval.
• Planner: The planner component devises a series of actions or tools the agent needs to execute to achieve the desired outcome. The planner can execute all actions at once or plan and execute them individually.
• Executor: The executor component is responsible for carrying out the actions or tools devised by the planner. It ensures that each action is executed correctly and waits for the outcome before proceeding to the following action if necessary.

Example Workflow of an AI Agent

1. Input Query: The user provides an input query.
2. Planning: The planner devises a series of actions or tools to achieve the desired outcome.
3. Execution: The executor carries out the actions or tools one by one, waiting for the outcome of each action before proceeding to the next.
4. Output: The agent produces the final output based on the executed actions.

4. Practical Applications of AI Agents

• Enhancing Customer Support: AI agents can enhance customer support by integrating tools for data retrieval, logic operations, and personalized responses. This ensures that customer queries are handled more efficiently and accurately.
• Automating Financial Analysis: AI agents can automate financial analysis by integrating data retrieval, calculations, and report generation tools. This can significantly reduce the time and effort required for financial analysis and reporting.
• Streamlining Content Creation: AI agents can streamline content creation by integrating tools for data retrieval, text generation, and content optimization. This can help generate high-quality content for marketing, blogs, and social media.
• Facilitating Decision-Making: AI agents can support strategic decision-making processes by integrating tools for data analysis, forecasting, and scenario planning. This can provide detailed insights and recommendations based on historical data and real-world information.

5. Famous Open Source Libraries for AI Agents

LangChain

LangChain is an open-source library designed to facilitate the development of large language models (LLMs) applications. It provides tools for integrating retrieval-based methods with generative models, making it easier to implement AI agent systems.

• Example: Adam uses LangChain to build a customer support chatbot that retrieves relevant information from the bank's knowledge base and generates accurate responses to customer queries.

Haystack

Haystack is another open-source library that supports the development of AI agent systems. It offers a range of features, including document retrieval, question answering, and generative response generation.

• Example: Adam leverages Haystack to create a system that retrieves financial reports and generates summaries for quarterly performance reviews.

Huggingface Agents

Huggingface Agents is an open-source library that provides a framework for building AI agents. It offers tools for integrating LLMs with various specialized functions, enabling the creation of sophisticated AI systems capable of handling a wide range of tasks.

• Example: Adam uses Huggingface Agents to develop an AI agent to perform complex financial analyses by integrating data retrieval, calculations, and report generation tools.

Adam's Insights: The Importance of AI Agents

Adam quickly realized the potential of AI agents in enhancing the bank's operations. By integrating specialized tools with LLMs, he could ensure that the AI systems were more effective in handling complex tasks. This would be crucial in customer support, financial analysis, content creation, and decision-making.

What's Next for Adam?

As Adam continues his journey into AI, his next step will be exploring the role of multi-agent systems in automating even more complex tasks. He will learn how to integrate multiple agents with LLMs and specialized tools to create sophisticated AI systems capable of autonomously handling various tasks.

Key Takeaways

• AI Agent Frameworks: These frameworks improve the work of large language models (LLMs) by adding special tools for different tasks.
• Limitations of LLMs: LLMs can struggle with logic, math, and searching for information, leading to mistakes.
• Understanding AI Agents: An AI agent combines an LLM with tools to perform specific tasks. It can do many tasks at once or one at a time.
• AI Agents Usage: Adam sees that using tools with LLMs can greatly improve efficiency in areas like customer service and finance.

Conclusion

AI agents are powerful systems that integrate LLMs with specialized tools to perform a wide range of tasks more effectively. By understanding and applying key techniques in AI agent frameworks, business analysts like Adam can harness the power of AI to drive innovation and efficiency in their organizations. As Adam continues his journey, he will explore the role of multi-agent systems in automating even more complex tasks, further unlocking the transformative potential of AI in the banking industry.