Agentic AI Unleashed: The Frameworks Powering the Next Wave of Intelligent Agents
Jothi Moorthy
IBM | Technology Leader | Generative AI Thought Leader | Driving AI Transformation at Top Organizations
Introduction
In this in this article, author Sireesha Ganti dives deeper into the world of agentic frameworks, exploring their key components, benefits, and real-world applications. She breaks down how these AI toolkits streamline the development of intelligent agents, enabling businesses to scale, enhance decision-making, and improve automation. Through a detailed examination of popular frameworks like BeeAI,? LangChain, AutoGen, and CrewAI, this article provides valuable insights for developers and organizations looking to leverage AI agents effectively.
Meet the Author
CSM Architect | Generative AI & Hybrid Cloud Strategist | Enabling Digital Transformation
Author Sireesha Ganti is a CSM Architect & Technical Specialist at IBM. She has background and expertise in working with clients across multiple domains implementing & designing solutions for facilitating digital transformation. Sireesha specializes in generative AI, automation technologies, and their practical applications, combining her passion for learning with technical writing, solution design and implementation. She is currently driving AI adoption, application modernization, and business automation for enterprise clients.
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Core Components of an Agentic Framework:
AI agent frameworks are like toolkits that help developers build powerful software applications and AI agents more easily and quickly. These toolkits provide pre-built components, libraries, tools or functions, and guidelines, so developers don't have to start writing code from scratch. These frameworks also help streamline the creation of AI agents and enable developers to focus on application logic instead of reinventing the wheel for agent memory & state management, for orchestration and for tool integration.
For instance:
Imagine you want to build an intelligent virtual assistant for a customer service scenario. Instead of writing the code from scratch, you can use one of the suitable frameworks that gives you tools to understand customer questions, identify the correct responses, and respond in natural language.
A robust agent framework must include the following key components:
1.???? Agent Architecture:
Frameworks must include a pre-defined architecture that can support a variety of agentic capabilities. Specifically,
2.???? Environment Interface:
Agentic frameworks must include communication protocols that facilitate agent-agent or agent-human interactions. Basically, all hardware & software that is necessary for the agent to interact with its environment. For example, in a self-driving car, sensors and cameras act as the environment interface, allowing the car to perceive its surroundings, collect data and perform actions.
3.???? Perception Module:
The function of perception within an agentic framework is to gather and process the data from external environment using sensors and APIs for applications involving image & character recognition, natural language processing, etc. For example, a virtual assistant receives user inputs (via text, voice), processes inputs, and determines the user’s intent, sentiment, etc.
4.???? Decision-Making Engine:
This component uses algorithms and machine learning models (rule-based, reinforcement learning, etc.) to make decisions using the processed data from perception module. For example, in a recommender system, the decision model outputs a recommendation on which product/service to recommend to the user based on user preferences & past behavior.
5.???? Learning Component:
This component uses machine learning models and/or LLMs to improve the agent performance over time by incorporating feedback and new data. For example, a virtual assistant improves its responses by learning from user feedback and interactions.
Other key components include integration mechanisms that allow agents to call external tools to perform a task; task management systems that allow coordination between agents’ tasks; simpler ways to define agents and tools; and most importantly monitoring capabilities to track and evaluate agent performance. All these components work together as a framework.
Why Agentic Frameworks?
Frameworks provide features and benefits that help the organizations build and deploy AI agents effectively across the board. Let’s take a closer look into some crucial aspects:
1.???? Scalability:
Agentic frameworks enable businesses to quickly add multiple AI agents to handle increasing workloads without building each agent from scratch. This scalability is crucial for organizations looking to expand their AI capabilities rapidly. Frameworks like Microsoft's AutoGen support the development of scalable AI agents that can handle large volumes of customer interactions and transactions, ensuring efficient and personalized service.
For example?, multiple AI customer service agents can be deployed rapidly during peak times at a banking institution in order to handle sudden increase in customer inquiries without having to build each new agent from scratch. ??
2.???? Consistency:
These frameworks provide a blueprint for AI agents, ensuring that all agents developed within the framework have consistent core capabilities and behaviors. This standardization is vital for maintaining quality across multiple AI agent deployments.
For example, a global e-commerce business can ensure that the AI-powered virtual agents provide uniform/similar responses & adhere to company policies across different geographic regions. In customer service, frameworks like Google's Dialogflow offer standardized approaches to building chatbots, ensuring consistent and reliable interactions across different platforms
3.???? Time and resource efficiency:
By offering pre-built components and defined parameters, agentic frameworks save significant time and resources that would otherwise be spent on developing AI agents from the ground up.
For example, agents can use pre-built components that agentic frameworks offer & avoid building from scratch.
4.???? Improved communication:
Agentic frameworks include communication protocols that facilitate interaction between AI agents, as well as between agents and human users. This enables seamless collaboration and more effective task completion.
For example, in a smart home system, multiple AI agents (e.g., for lighting, heating, and security) can seamlessly communicate and coordinate their actions using the protocols provided by the agentic framework.? Another example is where an autonomous vehicle makes driving decisions in real-time based on based on inputs from humans & environment.
5.???? Enhanced decision-making:
These frameworks often include reasoning engines that allow AI agents to analyze information, create goals, and plan different ways to achieve them. This capability makes the AI solutions more dynamic and adaptable to diverse scenarios.
For example, an AI agent in an autonomous vehicle can analyze real-time traffic data, weather conditions, and route options to make optimal driving decisions, adapting to changing circumstances on the road. In autonomous vehicles, frameworks like NVIDIA's Drive AGX allow seamless integration with sensors, cameras, and other hardware, enabling real-time data processing and decision-making
6.???? Monitoring:
Agentic frameworks typically come with built-in tools for monitoring and debugging, allowing developers to track performance and quickly identify and resolve issues. A software development team can quickly identify and resolve issues in their AI-powered code review system by using the built-in monitoring tools provided by the agentic framework
7.???? Flexibility:
While providing a structured foundation, agentic frameworks also offer the flexibility to customize AI agents for specific business use cases.
For example, a healthcare provider can customize an AI agent within the framework to handle patient scheduling, taking into account specific departmental needs and integrating with existing hospital systems.
It is important to note that while implementing an agentic framework can be valuable, it is also a tremendous overhead and therefore letting the business scenario drive the need for building AI agents using a framework should be the preferred approach and not vice versa.
For example, basic rule-based virtual assistant that answers FAQ on customer website might not need an entire agentic framework. Similarly, if the business is constrained on resources or if the scenario demands a highly specialized application, then a framework might not be the best approach.
Common AI Agentic Frameworks
There are several AI agentic frameworks available commercially.? In this section, we will explore some of the popular ones and their application.
BeeAI agent framework
Released in 2024, the BeeAI agent framework from IBM is an open-source toolkit designed for building agent-based workflows at scale.? BeeAI provides seamless integration with several LLM providers and tools.? BeeAI’s core capabilities include:
The BeeAI framework is optimized for large-scale operations in finance, healthcare, and supply chain domains.? The modular design allows for easy updates and replacement of individual agents with minimal impact on the agent system.? BeeAI also provides visibility into the agents’ thought process and is compatible with state-of-the-art LLMs like Granite and Llama.?
As with most of the frameworks, BeeAI can be resource intensive and requires substantial computational resources in order to handle multiple agents and complex workflows.
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Other popular agent frameworks
The following table summarizes some of the popular agent frameworks, their capabilities and applications:
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?Code Samples:
In this section, we will explore practical examples of building AI agents using various popular frameworks. The code samples demonstrate some of the core capabilities and implementation details of each framework, and help us understand how to leverage these tools effectively. These frameworks provide pre-built components that significantly reduce the development time. This ensures consistency and reliability in the agent code. In these code samples, you will see how different framework implementations are applied to a variety of business scenarios.
BeeAI framework example for building a multi-agent workflow
LangChain code sample for agent that calls a search tool??
LangGraph code sample for a chatbot with memory capability?
AutoGen code sample for a virtual assistant that self-initiates a chat session
CrewAI code sample for multiple agents executing a marketing strategy workflow
How to Choose the Right Framework
Selecting an optimal AI agent framework requires evaluating project complexity, developer expertise, language preferences, and architectural needs. Key technical considerations must include:
·????? Scalability (e.g., handling concurrent requests and large data volumes)
·????? Integration capabilities (with LLMs, tools/functions, frameworks)
·????? Specific features like multi-agent support
·????? Real-time processing, monitoring, and security measures.
Frameworks like AutoGen excel in complex multi-agent systems, while CrewAI is suited for rapid prototyping. LangChain offers flexibility for building custom AI agent applications, and Semantic Kernel provides a robust enterprise support.?
Assessing the framework's ability to handle natural language processing tasks, data ingestion methods, and output generation capabilities are critical because these capabilities are common to many business use cases. And finally, starting with basic implementations to identify core requirements before scaling to more complex architectures is a best practice in itself. Ultimately, the chosen framework must align with the technical & scalability requirements of the use case and team’s skill set.
Conclusion
In this article, we explored that AI agentic frameworks are essential for accelerating and standardizing development processes, and ensuring scalability in agents. Agentic Frameworks like BeeAI, LangChain, Microsoft AutoGen, Semantic Kernel, CrewAI, and LlamaIndex each offer unique strengths and capabilities and cater to a range of business use cases. When choosing the right framework, it's crucial to consider project requirements, integration capabilities, ease of use, and scalability. By aligning these factors with your specific goals, you can effectively leverage AI agentic frameworks to drive innovation and efficiency. Future research in this field is focusing on enhancing multi-agent collaboration, improving scalability, and addressing ethical considerations to ensure responsible AI deployment.
Getting Started
If you're looking to integrate LLMs into AI agents using IBM solutions, here’s how you can begin:
1?? Define the Role of Your LLM-Agent – Will it be an advisor, decision-maker, or fully autonomous agent? Clearly defining its role will help in selecting the right architecture.
2?? Leverage IBM Watsonx.ai for LLM Integration – IBM’s Watsonx.ai provides a powerful platform to deploy, fine-tune, and scale large language models (LLMs). While Watsonx.ai itself is not an agent-building tool, it serves as the cognitive layer that can be integrated into AI agents to enhance reasoning, natural language understanding, and decision-making.
3?? Implement Context & Memory Management with Watson.data and Milvus – LLMs require efficient context management. Use IBM Watson.data for structured data storage and Milvus for managing vector databases to enable retrieval-augmented generation (RAG), ensuring agents retain knowledge over time.
4?? Enhance Real-World Interaction with Watsonx Orchestrate – IBM Watsonx Orchestrate enables AI agents to interact with enterprise applications, automate workflows, and execute tasks autonomously, serving as an orchestration layer for LLM-powered agents.
5?? Optimize & Govern AI Performance with IBM Watsonx.governance – To ensure AI compliance, fairness, and risk mitigation, leverage IBM Watsonx.governance to monitor and manage AI agent behavior, track decision-making processes, and ensure regulatory adherence.
?? Looking to build your own AI-powered agent? Start by integrating LLMs with Watsonx.ai, manage knowledge with Watson.data & Milvus, automate workflows with Watsonx Orchestrate, and ensure governance with Watsonx.governance.
References:
Disclaimer
This article is written by?@Sireesha Ganti and published in the?Gen AI Trends & Applications?newsletter with their authorization. The content has been shared by the author for publication, with any modifications made solely for clarity and formatting. The views and opinions expressed are those of the author and do not reflect the official policies or positions of IBM or any other organization.This content is for?informational and educational purposes only?and should not be considered financial, legal, or professional advice. AI systems, particularly those leveraging large language models (LLMs), come with inherent risks, including biases, limitations in real-time adaptability, and ethical considerations. Organizations looking to deploy AI solutions should conduct thorough testing, adhere to governance frameworks, and ensure compliance with industry regulations.Some images in this article may be?AI-generated. All efforts have been made to ensure accuracy and proper attribution.By engaging with this content, readers acknowledge that the authors and publisher are not responsible for any decisions made based on the information provided.