1. Agentic AI and Its Key Components
2. Why are Agentic AI Frameworks Important?
3. Top Agentic AI Frameworks You Should Know in 2025
4. Why Partner with Tx?
5. Summary

From a single AI monitoring and flagging scams for banks to a multiagent system supporting inventory optimization and management, agentic AI is proving to be a game-changer for businesses. In the last couple of years, the world has witnessed rapid advancements in the AI domain. Whether it’s generative AI, contextual chatbots, or robots learning through observation, AI is advancing rapidly and impacting our daily lives. Agentic AI is the recent buzz with immense potential to empower systems with independent decision-making abilities as they learn from collaboration and experience.  

As we are already in 2025, enterprises need frameworks to help them streamline AI workflows and complex decision-making processes. But before we talk about top Agentic AI frameworks in 2025, let’s understand what they are and why they are essential. 

Agentic AI and Its Key Components 

Agentic AI, AI Agents, or autonomous agents are programs capable of performing tasks autonomously without human help. These agents can construct a detailed plan to complete a complex task. The function calling step helps them connect with external tools like APIs, web searches, data sources, and other AI agents. These autonomous agents can learn from received feedback and store the data in their memory for future reference.  

Enterprises use various programming languages, such as JavaScript, Python, etc., to build AI agents. However, the quicker way is to use Agentic AI frameworks, which serve as platforms for developing, releasing, and managing AI agents.

Key components of AI agent frameworks include: 

• An agent architecture for defining the AI agent’s internal processes, such as decision-making, memory units, and engagement capabilities. 

• Tools for connecting AI agents with their respective operating environments (real-world or simulated). 

• Task management systems are used to manage the task completion status of agents. 

• Communication protocols for facilitating engagement between agents and between humans and agents. 

• ML algorithms for enabling learning mechanisms for agents so they can improve their performance. 

• Integration tools that connect agents with APIs, external data sources, and more. 

• Monitoring and debugging protocols to track agents’ performance, issues, and behavior. 

Why are Agentic AI Frameworks Important?

Agentic AI frameworks assist in streamlining the development of complex AI agents. These frameworks consist of tools and mechanisms organizations use to develop intelligent agents that can autonomously think, implement, and upgrade knowledge. They also streamline the development process with pre-built components, APIs, and libraries that facilitate quick AI integration into applications. In the AI field, AI agent frameworks are necessary for several reasons: 

• Pre-built components streamline AI agent development, reducing time and effort. 

• Consistent methodologies enhance collaboration and knowledge sharing. 

• Supports everything from single-agent apps to complex multi-agent systems. 

• Simplifies AI development, making advanced techniques more approachable. 

• Automates basic tasks of AI agent development, enabling focus on new tech innovations.  

Top Agentic AI Frameworks You Should Know in 2025 

CrewAI 

CrewAI is a lean Python framework that empowers organizations with precise low-level control and high-level simplicity for developing autonomous AI agents. It is also open source with role-based architecture that treats agentic AI as a “crew” of “workers.” Here are its key features: 

• Facilitate human-like collaboration between agents 

• Build systems that can handle dynamic and team-based activities 

• Built-in delegation and task-mapping features 

• Industry-specific and customizable modules for AI agent development 

• Supports connections with various LLMs like Google Gemini, Claude, and OpenAI GPT models. 

• Can be accessed on GitHub 

Microsoft AutoGen 

AutoGen specializes in orchestrating multiple AI agents and helps build autonomous, event-driven systems by solving complex issues in a distributed environment. It offers support for developing systems requiring multi-agent engagement, which makes it an ideal tool for cloud automation and IT infrastructure management. Microsoft AutoGen is an open-source tool with features such as: 

• Support for LLMs and conventional APIs 

• Easy to integrate with external services and tools 

• Multi-agent conversation framework with flexible flow control 

• Integrated task recovery and error-handling mechanisms 

• Agent roles and behaviors customization 

• Advanced task prioritization and reasoning 

• AutoGeen Bench tool for assessing and benchmarking AI agent’s performance 

• AutoGen Studio for a no-code interface to create agents 

Smolagents 

Smolagents is an open-source Agentic AI framework that equips organizations with a toolkit for developing intelligent multi-agent systems. It focuses on collaboration, flexibility, and modularity to enable developers to create sophisticated AI systems that can work in collaboration and independently. Its key features include: 

• Advanced management systems to maintain context state across interactions 

• Dynamic workflow orchestration for task management 

• Modular multi-agent architecture 

• Facilitate easy integration with multiple language models and APIs 

• Facilitate inter-agent transmission with robust communication protocols 

• Support autonomous and human-supervised workflows 

AutoGPT 

AutoGPT is a GPT-4-based language model that executes objective-defined activities via prompt input. This model rapidly advances the autonomous AI agents wave by improving decision-making and integrating sophisticated functionalities. Its key features include: 

• Enables seamless interaction between multiple agents to solve complex problems 

• AI agents plan, prioritize, and execute tasks independently 

• Stores previous interactions to improve future decision-making 

• Adjusts actions based on real-time feedback and changing goals 

• Seamlessly connects with external APIs, databases, and software for enhanced automation 

• Understands and generates human-like text for better interactions 

• Improves performance over time through continuous learning 

LangChain 

LangChain is another open-source framework that builds LLM-powered applications like AI agents and chatbots. Its modular architecture represents abstractions of complex concepts and steps needed to work with LLMs. Developers can chain tools, prompts, and memory into coherent pipelines, allowing them to build complex LLM-powered apps quickly. Its key features include: 

• Supports interactions between multiple AI agents to tackle complex workflows 

• Connects with APIs, databases, web search, and other external tools for enhanced functionality 

• Uses advanced techniques like self-reflection and step-by-step reasoning for better decision-making 

• Enhances responses by integrating real-time data retrieval from knowledge bases 

• Allows developers to fine-tune and extend capabilities based on specific business needs 

• AI agents analyze tasks, generate solutions, and execute them with minimal supervision 

• Retains and recalls past interactions to improve contextual understanding and response accuracy 

Why Partner with Tx? 

At Tx, we understand the significance of agentic AI in the business environment. Our TxLabs.ai is at the core of our innovation efforts, where we combine cutting-edge AI capabilities with automation expertise to deliver robust agentic AI solutions. We empower your business by achieving operational excellence, reducing costs and inefficiencies, and scaling AI agents intelligently to keep you at the lead in a fast-evolving market. We leverage agentic AI frameworks like CrewAI to assist you with building tailored AI agents that can sync with your business strategy.  

Our AI-driven approach enables autonomous test execution, generating cases and identifying gaps with minimal supervision. We use predictive analytics to detect defects and risks, ensuring high product quality. AI optimizes workflows, reducing operational overhead and accelerating time-to-market. Additionally, self-healing mechanisms refine test suites by eliminating redundant cases and focusing on critical areas. This ensures reliable QA cycles, improved performance, and seamless process optimization. 

Summary 

Agentic AI is transforming businesses by enabling autonomous decision-making and optimizing workflows. In 2025, enterprises need robust Agentic AI frameworks to streamline AI development and enhance efficiency. Leading frameworks like Microsoft AutoGen, Smolagents, CrewAI, AutoGPT, and LangChain empower AI agents to automate tasks, integrate with APIs, and improve decision-making. Tx’s AI-driven approach ensures cost reduction, operational excellence, and scalability, keeping businesses ahead in a rapidly evolving market. To know how partnering with Tx will help you harness the full potential of Agentic AI, contact our experts now.