Generative AI Agents: Introduction and Application at Evoya AI

Introduction to Generative AI Agents

While traditional AI systems are often rigid and rule-based, unable to even determine which steps to execute next, generative AI agents offer a new dimension of flexibility and autonomy. These agents are capable of generating human-like content and independently handling complex tasks, similar to humans. Generative AI Agents are fascinating "entities" with more independence than classical AI systems.

In this blog post, you will learn everything about generative AI agents, including their functionality and how we at Evoya AI utilize this technology.

Technical Foundations of AI Agents

Algorithm of a LangChain Agent

To better understand the concept of generative AI Agents, let's consider the example of LangChain Agents. LangChain agents are systems that use a language model (Language Model, LLM) to interact with various tools and perform tasks such as answering questions, interacting with APIs, or making decisions based on the model's results. The core of LangChain Agents is to use the language model as a decision engine to determine which actions should be executed in which order, as opposed to hard-coded action sequences in traditional chains.

Components of a LangChain Agent

• Language Model (LLM): The language model is used to make decisions about which actions should be executed. Thanks to LangChain, various LLMs such as OpenAI GPT 4o, Claude Sonnet, or the open-source model Llama 3 (Meta) can be used.
• Tools: These provide additional functionalities for tasks such as data retrieval or processing or execution of actions
• Memory: Enables the agent to store and retrieve information to maintain context during decision-making.

Algorithm Steps

1. LLM Initialization: Configuration of the language model with parameters such as temperature, which controls the randomness of the model's outputs.
2. Loading Tools: Selecting and loading tools that provide the agent with additional functionalities. These tools can be either provided by LangChain or custom-built.
3. Agent Initialization: Instantiation of the agent with the initialized LLM and loaded tools. This can be done with the initialize_agent function, which simplifies the creation of the agent with default settings.
4. Agent Execution: The agent executes a chain of actions based on a given prompt, interacts with tools, and uses the decision-making capabilities of the language model. The agent observes the results of each action, repeats the process as needed, and ultimately delivers a final answer to the original input.
5. Memory Management: During execution, the agent uses memory to store information and maintain context for personalized and coherent interactions. Memory helps the agent understand the context of the conversation, accumulate knowledge over time, personalize responses, and ensure continuity in interactions.

Example Implementation of a LangChain Agent

Here is an example of how to implement a LangChain agent with multiple tools and multi-input tools:

from langchain.agents import initialize_agent, AgentType
from langchain.llms import OpenAI
from langchain.tools import Tool
from langchain.memory import ConversationBufferMemory
# Initialize the language model
llm = OpenAI(temperature=0)
# Define the tools
search_tool = Tool(
name="search",
func=lambda query: f"Searching for {query}",
description="Tool to search the web"
)
calculator_tool = Tool(
name="calculator",
func=lambda expression: eval(expression),
description="Tool to perform calculations"
)
# Initialize the agent with tools
agent = initialize_agent(
tools=[search_tool, calculator_tool],
llm=llm,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
memory=ConversationBufferMemory()
)
# Execute the agent with a prompt
prompt = "What is the weather in Zurich and what is 5 + 3?"
response = agent.run(prompt)
# Output the answer
print(response)

Source: LangChain Documentation – Quick Start

REACT: Reasoning and Acting

An important aspect of how LangChain Agents function is the REACT model, which stands for "Reasoning and Acting". This model enables the agent not only to act based on predefined rules but also to make complex considerations and decisions based on context and available information.

Example Process

The user asks the question but sees nothing of the agent's intermediate steps. The agent only returns the final answer to the user at the end.

User: What is the weather in Zurich and what is 5 + 3?
Agent:
1. Reasoning: To answer the question about the weather in Zurich, I need to perform a web search.
2. Acting: Use the 'search' tool to search for the weather in Zurich.
3. Result: The current weather in Zurich is sunny at 25 degrees Celsius.
4. Reasoning: To answer the second question, I need to perform a calculation.
5. Acting: Use the 'calculator' tool to perform the calculation 5 + 3.
6. Result: 5 + 3 equals 8.
7. Final Answer: The weather in Zurich is sunny at 25 degrees Celsius and 5 + 3 equals 8.

Source: LangChain Documentation – Zero Shot React Description

Additional Agent Models in LangChain

Besides the REACT model, LangChain offers a variety of other agent models optimized for different use cases:

• Zero-Shot ReAct: A model without memory, suitable for one-time requests.
• Conversational ReAct: A model with memory that maintains context across multiple interactions.
• ReAct Docstore: A model specifically developed for interaction with document stores.
• Self-ask with Search: A model that independently asks questions and searches for answers.

Source: LangChain Documentation – Agent Types

How We Use Agents at Evoya AI

At Evoya AI, we deploy generative AI agents as the heart of the Evoya AI platform to efficiently and intelligently solve a variety of tasks. We integrate these agents into our systems to provide our customers with tailored solutions. Our agents are capable of interacting with various tools, including connections to CRM or ERP systems, which is particularly beneficial for our corporate clients. As a special feature, we use specialized agents in multiple stages as a cooperating team.

We constantly evaluate the latest approaches and technologies to ensure that our agents are always up to date. If you would like to learn more about how we deploy generative AI Agents and how they can help your business, we are happy to offer a demo. Contact us here.

Further Reading

For those who want to delve deeper into the topic, there are some notable research papers and studies on generative AI agents:

1. Generative Agents: Interactive Simulacra of Human Behavior
   This work introduces generative agents that simulate human behavior. Link to the study
2. Generative AI Agent for Next-Generation MIMO Design
   This study examines generative AI agents in the context of MIMO system design. Link to the study
3. Generative AI at Work
   This study analyzes the introduction of generative AI-based conversational assistants in customer service. Link to the study