Agentic Frameworks for Generative AI Applications

Introduction

Imagine having an AI-powered assistant that not only responds to your queries but also autonomously gathers information, executes tasks, and even handles multiple types of data—text, images, and code. Sounds futuristic? In this article, we dive into the autogen framework, a cutting-edge technology that enables you to build such intelligent, multimodal conversational agents. Whether you’re looking to automate business development tasks like web scraping and summarizing content or even execute code with human oversight, this guide will walk you through every step. If you’re interested in leveraging AI to create powerful, self-managing agents, this is a must-read!

This article is based on a recent talk given by Sudalai Rajkumar on Agentic framework for GenAI Applications, in the DataHack Summit 2024.

Learning Outcomes

• Understand the core concepts and components of Agentic AI.
• Learn the benefits and limitations of traditional AI compared to Agentic AI.
• Explore the role of tools and systems in enhancing AI agents’ capabilities.
• Discover the applications and potential impact of multi-agent systems.
• Examine ethical considerations and future trends in Agentic AI.

What is Agentic AI?

Agentic AI refers to a category of artificial intelligence systems designed to act with a degree of autonomy and agency. Unlike traditional AI models that primarily operate under direct human supervision, Agentic AI frameworks are built to handle complex, real-world tasks with minimal intervention. These systems are capable of managing various components like conversational agents, web search tools, and code execution environments. They use advanced technologies to process multiple types of data—text, images, and even executable code—enabling them to perform sophisticated functions such as gathering information, interacting with users, and executing tasks in real-time.

One prominent example of Agentic AI is the autogen framework, which supports the development of intelligent agents capable of searching the web, summarizing content, and executing code. This framework offers a structured approach to building agents that can handle multimodal inputs and complex conversational patterns, making it an invaluable tool for developers and businesses looking to automate intricate processes.

Also Read: A Deep Dive into LangChain’s Agent Framework

Why is Agentic AI Important?

Let us now understand why is Agentic AI important.

Dynamic Interaction and Autonomy

Unlike traditional Large Language Models (LLMs), which generate responses in a zero-shot mode, agents interact dynamically. Traditional LLMs create tokens based on prompt inputs without the capability to revisit or modify their output. In contrast, agents can continuously refine their responses. They do this based on new information, feedback, or changes in context. This allows for more adaptive and autonomous problem-solving.

Enhanced Knowledge Integration

LLMs are inherently limited by their pre-existing internal knowledge, which might not cover all relevant or up-to-date information. Agents, however, can be designed to access and integrate real-time data from various sources, enhancing their ability to provide accurate and current information. This makes them more effective in environments where up-to-date knowledge is crucial.

Action Execution Capability

Traditional LLMs lack the ability to execute actions, such as running code or performing specific tasks beyond generating text. Agents can bridge this gap by incorporating functionality to execute code, interact with other systems, or perform complex actions directly. This capability is essential for automating tasks and executing workflows that involve more than just generating text.

Complex Task Handling

LLMs are often not suitable for performing complex, multi-step tasks that require intricate processes or decision-making. Agents can handle such tasks by combining various functionalities—like accessing external databases, interacting with APIs, and performing sequential operations—making them ideal for complex and multifaceted applications.

Also Read: Comprehensive Guide to Build AI Agents from Scratch

Understanding Components of AI Agents

We will now dive deeper into understanding components of AI Agents.

User Request

This is where it all begins. The user provides an input or prompt, which serves as the basis for the agent’s actions. Unlike traditional AI models that might respond with a static answer, agents are designed to take this request and interact dynamically with the environment, adapting their behavior and output based on user instructions.

Agent

The central figure in this system, the agent processes the user request and orchestrates the necessary actions. The agent acts autonomously to interpret the input, manage resources, and make decisions on how to proceed. It’s not just about generating a response; it’s about understanding the goal and determining the steps needed to achieve it, often by breaking down complex tasks into manageable subtasks.

Memory

Memory is crucial for agents to retain context and learn from previous interactions. Unlike traditional LLMs, which don’t have persistent memory across interactions, agents can store relevant information and recall it as needed. This allows them to track user preferences, project goals, or ongoing tasks, creating a more personalized and coherent experience.

Tools

Tools extend the agent’s capabilities beyond just generating text. These could be APIs, databases, external software, or systems that the agent can access to complete tasks. For instance, an agent might use a code execution tool to run a program, or a data retrieval tool to gather real-time information. These tools enable the agent to perform actions in the real world, enhancing its functionality far beyond static responses.

Planning

Planning allows agents to break down a user’s request into structured steps. Instead of providing a single response to a complex problem, the agent devises a plan of action. The agent predicts which tools to use, what information to recall, and what the final outcome should be. This systematic approach ensures that the agent can handle tasks requiring multiple stages. It makes the agent suitable for more intricate and prolonged workflows.

What are Single Agent Systems?

In a Single Agent System, one agent is tasked with managing and fulfilling user requests. The agent is responsible for understanding the input, processing it, and determining the steps necessary to deliver the desired outcome. This centralized model allows the agent to operate independently, focusing on one task at a time with a clear objective.

One of the key features of single agent systems is tool usage. The agent is equipped with access to various external tools to extend its capabilities. For example, when presented with a task that requires coding, the agent can execute code by utilizing code execution tools. It may also interact with APIs, databases, or external software to gather information, perform calculations, or generate outputs. The agent selects the appropriate tools based on the task requirements and uses them autonomously to achieve the goal.

A Single Agent System ensures that tasks are handled efficiently and within a controlled environment. This makes it highly suitable for more straightforward and focused workflows. By leveraging its internal memory and external tools, the agent can tackle diverse challenges. It maintains coherence and task accuracy throughout the process.

Tools for Agents

Agents rely on a range of tools to extend their capabilities beyond their internal knowledge and processing power. These tools empower agents to execute tasks, retrieve information, and interact with external systems effectively. Here are some key tools commonly used by agents:

Vector Databases

Vector databases play a crucial role in enabling agents to store, retrieve, and process vast amounts of information in a format optimized for similarity searches. When an agent needs to remember past interactions, complex data points, or large datasets, vector databases help in quickly identifying relevant information based on similarity rather than exact matches. This is particularly useful when the agent deals with natural language inputs or requires advanced pattern recognition.

Web Search

Web search tools allow agents to access real-time information from the internet, expanding their knowledge base beyond pre-existing internal data. When faced with questions or tasks that require the latest updates, facts, or insights, the agent can perform web searches to gather relevant content. This capability is essential for dynamic problem-solving, enabling the agent to adapt to new information and respond accurately in real-world scenarios.

Code Execution

Code execution tools enable agents to write, test, and run code as part of their problem-solving process. For tasks involving programming, such as generating scripts or automating workflows, the agent can execute code in real-time. This ability allows agents to tackle complex technical challenges. These include debugging, software development, and automation.

External APIs

Agents use external APIs (Application Programming Interfaces) to interact with various systems, services, and platforms. By accessing external APIs, agents can retrieve data, trigger actions, or communicate with other software. Whether it’s fetching weather data, initiating financial transactions, or integrating with enterprise systems, APIs serve as a bridge that allows agents to perform specialized tasks across different domains and industries.

What are Multi-Agent Systems?

Multi-Agent Systems (MAS) bring together multiple agents to work collaboratively, each with specialized skills or roles, to solve complex tasks that are beyond the capacity of a single agent. These systems enable a more dynamic and distributed approach to problem-solving, allowing agents to interact, share knowledge, and coordinate actions to achieve a common goal.

In a multi-agent setup, each agent is designed to handle a specific task or process within a broader context. This division of labor leads to greater efficiency, as agents can operate independently and in parallel, ensuring faster task completion and enhanced scalability.

Key Benefits of Multi-Agent Systems

• Specialization: Agents can be designed to specialize in specific areas, such as web searching, data retrieval, or code execution. Each agent focuses on a particular domain, allowing for more precise and accurate handling of tasks.
• Collaboration: By working together, agents can share information, align on goals, and support each other in complex problem-solving. One agent might gather data while another processes it, creating a more robust and flexible system.
• Resilience: If one agent fails or encounters an issue, other agents can step in, ensuring that the task can still be completed. This creates a more resilient system with built-in redundancy.
• Scalability: Multi-agent systems are scalable, making it easier to add more agents as tasks grow in complexity. As demands increase, additional agents can be introduced to balance the workload.

Tool Usage in Multi-Agent Systems

Tools like vector databases, external APIs, and code execution come into play in multi-agent systems. For example, one agent may use a vector database to retrieve relevant information, while another agent might use an API to fetch real-time data. These tools enable the agents to work efficiently, making it possible to handle more intricate and multi-faceted tasks.

Two Agent Systems – Reflection

In a Two-Agent System, the idea revolves around two distinct agents working together, each having a unique role to reflect on and refine tasks. This reflective nature is crucial for complex tasks that require iterative processes and dynamic adjustments.

One agent typically takes on the role of performing the primary task, such as generating text, executing code, or retrieving data. Meanwhile, the second agent acts as a reflective entity, reviewing the outputs, providing feedback, and suggesting refinements. This process of reflection is essential to improve the overall quality of the work, ensuring that the first agent can learn from past actions and make better decisions moving forward.

For instance, in the context of code execution, the first agent might generate code based on a given task, while the second agent reviews the code, checks for potential errors or inefficiencies, and prompts revisions. This back-and-forth dynamic enables continuous improvement and higher-quality results.

Reflection in two-agent systems helps overcome the limitations of traditional AI models, where feedback loops are often absent. The reflective agent ensures that tasks aren’t just completed but refined for maximum efficiency, creativity, and accuracy. This collaboration leads to better performance across tasks like code generation, data retrieval, and problem-solving processes.

Multi-Agent Systems – Group Chat

In Multi-Agent Systems, agents collaborate to solve complex problems by distributing tasks among themselves. In a group chat environment, multiple agents work in parallel, communicating and sharing knowledge. Each agent contributes to a specific part of the task. This system enables collective problem-solving, with agents specializing in different areas. As a result, tasks are completed more quickly and efficiently.

For instance, one agent might handle web search tasks, another might be responsible for code execution, while a third might focus on interacting with external APIs. These agents can communicate and share their findings, contributing to a broader goal. The group chat dynamic enables each agent to understand the overall objective, break it down into smaller components, and then come together to provide a holistic solution.

The group chat setting is useful for tasks needing various forms of expertise or resources. Agents leverage each other’s strengths and knowledge bases. Constant communication ensures that agents stay aligned on the end goal. They adjust their strategies in real-time based on insights from fellow agents. This creates a collaborative ecosystem that mimics human teamwork, with added benefits of automation and scalability.

Understanding Agentic Frameworks

Agentic frameworks are specialized software platforms or packages designed to facilitate the creation, management, and deployment of AI agents. These frameworks provide pre-built components and abstractions that simplify the process of building agentic systems, allowing developers to focus on higher-level tasks rather than reinventing foundational elements.

Key features of agentic frameworks include:

• Pre-built Components and Abstractions: These frameworks offer essential building blocks to help developers quickly set up agents and workflows. They define common design patterns and workflows to streamline the creation of AI systems.
• Integration with Tools and Environments: Agentic frameworks are designed to work seamlessly with a variety of external tools and environments, enabling agents to interact with databases, APIs, and other services needed for complex tasks.
• Communication between Agents: The frameworks support multi-agent communication, allowing agents to collaborate, share information, and work together on larger tasks. This feature is particularly crucial in multi-agent systems, where coordination is key.
• Memory Management: Handling memory effectively is essential for agents to perform tasks requiring context retention over time. Agentic frameworks provide mechanisms to manage and access memory, ensuring that agents can recall relevant information when needed.
• Monitoring and Debugging: These platforms often include built-in tools for monitoring agent performance, tracking workflows, and debugging, ensuring that agents are functioning as expected and enabling easier troubleshooting.

Also Read: Top 5 Frameworks for Building AI Agents in 2024

Agentic Framework – PhiData

The Agentic Framework by PhiData empowers users to build advanced AI assistants. It goes beyond large language models (LLMs). PhiData integrates memory, knowledge, and a suite of tools. This enhances the capabilities of AI assistants. It makes them more effective at handling complex tasks.

In the PhiData framework, an AI Assistant is a combination of several key components:

LLM (Large Language Model): The core of the assistant, responsible for processing natural language and generating responses.

• Memory: This allows the assistant to retain information over time, enabling it to maintain context and improve its responses by recalling past interactions.
• Knowledge Sources: These include a variety of data inputs such as chat history, PDFs, websites, and databases that the assistant can refer to when providing responses.
• Tools: The assistant is equipped with powerful tools to perform actions beyond just answering questions. These tools include:
  • Web Search: To find information in real-time.
  • Send Email: Allowing the assistant to handle communication tasks.
  • Summarize Documents: Offering the ability to condense information from large texts.
  • Run Queries: Interacting with databases and running specific queries to retrieve relevant data.
• Entities: The assistant can work with structured data such as JSON, make API calls, and use facts or stored text to inform its responses.
• Workflows and Triggers: PhiData assistants can trigger workflows, such as database actions or vector database operations (VectorDB), to automate complex processes.

Agentic Framework – CrewAI

The CrewAI Framework is specifically designed to enable the creation and management of role-playing AI agents that work together as a cohesive unit to tackle complex tasks. It provides a structured approach to building and deploying AI agents that can operate in a coordinated and collaborative manner.

Key Features of CrewAI Include

• Role-Based AI Agents: CrewAI facilitates the design of AI agents with specific roles, allowing them to work together within a defined structure. These agents can be assigned specialized tasks depending on their capabilities, enabling efficient division of labor.
• Customizable Tools: Users can define the tools that each AI agent will use, customizing them based on the requirements of the tasks at hand. This flexibility allows agents to leverage the right set of tools to perform their functions effectively.
• Task Assignment and Execution: CrewAI supports the ability to define task execution processes that can be either sequential or hierarchical, depending on the complexity of the workflow. This ensures tasks are completed in the correct order or as part of a larger structured plan.
• Output Management: The framework allows agents to save their outputs as files, making it easy to retrieve and review the results of their work. This is particularly useful for creating documentation or logs of task completion.
• Open-Source Model Compatibility: CrewAI is designed to work with open-source models, providing flexibility for users who prefer to integrate a variety of AI models into the framework. This makes it accessible to a broader range of developers and use cases.

CrewAI enables teams of AI agents to work together, taking on specialized roles and tasks in a seamless, organized, and collaborative environment.

Agentic Framework – AutoGen

AutoGen is an open-source programming framework developed by Microsoft to facilitate the building and deployment of AI agents. It provides a flexible platform that allows developers to customize AI agents for a wide range of tasks and use cases. The framework is particularly well-suited for complex multi-agent workflows, providing robust support for conversation patterns and interactions.

Key features of AutoGen Include

• Customizable AI Agents: AutoGen allows AI agents to be tailored to meet various needs, making it adaptable for diverse tasks and industries. Users can modify agent behavior, tools, and workflows based on specific requirements.
• Support for Complex Multi-Agent Workflows: The framework supports advanced conversation patterns that enable multiple agents to work together seamlessly in complex scenarios. These multi-agent workflows make it ideal for large-scale operations where multiple tasks need to be coordinated.
• Human-in-the-Loop Interaction: AutoGen integrates human oversight into the process, allowing for human-in-the-loop interactions. This ensures that critical decisions can be made by a human operator, enhancing the reliability of AI systems.
• Code Execution Support: AutoGen provides robust support for code execution, allowing AI agents to execute scripts or programs within a local environment or via Docker containers. This makes it suitable for technical tasks like automation, data analysis, or software development.
• Conversational Memory and Context Management: AutoGen is equipped with conversational memory capabilities, enabling AI agents to remember past interactions and maintain context over long conversations. This is crucial for maintaining continuity in dialogues, especially in customer service or collaborative environments.
• Built-in Error Handling: The framework comes with built-in error-handling mechanisms to ensure smooth operation even when unexpected issues arise, enhancing the system’s reliability and resilience.

The image below is a configuration for an AI system where agents interact without human input (human_input_mode="NEVER") and handle tasks autonomously. It includes agents like ConversableAgent, AssistantAgent, and UserProxyAgent managed by a GroupChatManager, enabling group chat interactions with the option for human input if needed (human_input_mode="ALWAYS").

The multi-agent AI system uses specialized agents like Assistant, Expert, and Commander to tackle various tasks, from math problem-solving to dynamic group chats and multi-agent coding. It facilitates seamless collaboration and communication between AI and human participants.

Use Cases of Agentic AI

Let us now discuss the use cases of Agentic AI.

Automated Problem Solving and Decision Making

Agentic AI can autonomously solve complex problems by utilizing multiple specialized agents. For instance, one agent could be dedicated to retrieving relevant data, another to analyzing that data, and a third to make decisions based on the findings. This approach is highly effective for dynamic decision-making scenarios like risk assessment or project planning.

Collaborative Multi-Agent Coding

In this use case, Agentic AI enables multiple agents to collaborate on coding tasks. Agents can be assigned specific coding responsibilities, such as retrieving data, writing code snippets, or executing tests, all while maintaining communication. This multi-agent approach optimizes complex programming tasks, reducing the time and errors often associated with manual development.

Dynamic Group Chats

Agentic AI supports dynamic group chats where multiple agents work together to communicate and share information. These chats can involve humans or other AI systems, enabling efficient task coordination. Whether in customer support, collaborative work environments, or education, agents can handle various tasks like answering queries, moderating discussions, or organizing data.

Conversational Games like Chess

One specific use case is conversational chess. In this scenario, Agentic AI supports both human and AI players. The agents manage game logic and provide strategic suggestions. They also handle moves during the game. This creates a rich, immersive experience for users. It enhances both learning and engagement.

Complex Task Execution with Custom Tools

Agentic AI systems can execute tasks with the help of customizable tools. For instance, agents can send emails, run queries, or call APIs. This enables automation of repetitive or complex workflows, such as business operations or software development, with efficiency and precision.

Also Read: A Comprehensive Guide on Building AI Agents with AutoGPT

Future of Agentic AI

The future of Agentic AI envisions systems that will increasingly operate with autonomy, leveraging advanced capabilities like multi-agent collaboration and enhanced tool integration. These AI systems will continue to evolve to handle more complex tasks, improve decision-making, and deliver more accurate results.

We can expect Agentic AI to expand into fields like healthcare, finance, and education. In healthcare, specialized agents can assist in diagnostic processes. In finance, they can aid in financial analysis. And in education, they can provide personalized learning experiences. The growing ability of AI agents to learn from experiences will shape future developments. They will bring greater efficiency and intelligence to various industries.

Ethical Considerations of Agentic AI

Agentic AI introduces several ethical challenges, particularly in terms of decision-making and autonomy. As agents take on more responsibilities and operate independently, there’s a risk of unintended consequences if they act without sufficient oversight. Concerns about accountability also arise—if an AI agent makes a harmful decision, it’s unclear who should be held responsible. Additionally, the potential for AI agents to perpetuate biases in data or decisions remains a key issue. Ensuring transparency and fairness in how agents process information is critical to mitigating bias and ensuring ethical AI systems.

Potential Impact of Agentic AI on Society

Agentic AI holds significant potential to transform society by automating many of the tasks that currently require human labor. This could lead to increased efficiency and productivity, particularly in sectors like customer service, healthcare, and education. However, the widespread deployment of Agentic AI also raises concerns about job displacement, as AI systems take over roles traditionally performed by humans.

On the positive side, Agentic AI could empower individuals and organizations to solve complex problems faster and more effectively, leading to innovations across industries. The potential societal impact will depend on how well we address challenges related to job transition, ethics, and equitable access to AI technologies.

Conclusion

Agentic AI represents a significant leap forward in the capabilities of artificial intelligence, enabling more autonomous, intelligent systems to handle complex tasks and adapt to various environments. As AI agents continue to evolve, they will play a crucial role across multiple industries, from healthcare to finance, offering efficiency, innovation, and new solutions to real-world problems. However, with this advancement comes the need for careful ethical considerations, addressing challenges like accountability, bias, and societal impact. As we navigate the future of Agentic AI, balancing its potential with responsible deployment will be key to ensuring its positive contributions to society.

Frequently Asked Questions

Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.