It seems like more businesses than ever are adopting AI agents to automate complex business processes and workflows, with one survey reporting over 50% of respondents use some form of AI agents today.
Perhaps you're trying to understand whether (or which) agentic framework is best for you. You’re curious to learn how an agentic infrastructure can provide the tools and structure to build complex systems where multiple agents can interact, communicate, and work together to achieve a common goal.
But what are the key features and benefits of agentic frameworks? And how can you choose the right agentic AI framework from so many options? We’ll discuss all of this and more, from the basics of agentic frameworks to essential considerations to your implementation.
Whether you’re a developer evaluating different frameworks or a business leader exploring agentic solutions, you’ll learn how these frameworks let you develop customized agentic systems that can operate and make decisions autonomously – and deliver ultra-efficient automations across your organization.
What is an agentic framework?
An agentic framework provides a foundational structure for developing autonomous systems. It provides a set of defined parameters and protocols that handle the interactions between AI agents, which can be LLMs or other types of software. They provide mechanisms for agents to:
- Communicate with each other (exchange messages, share information)
- Coordinate their actions (work together on a task, avoid conflicts)
- Reason about their environment (understand the context they are in)
- Make decisions (based on their goals and the information they have)
Essentially, an agentic framework is a blueprint outlining how AI components—such as perception modules, planning algorithms, and decision-making processes—should work together to achieve specific goals.
Developers employ these frameworks to guide the creation of custom AI agents, ensuring their solutions can communicate, reason, and execute tasks accurately within enterprise environments.
Key features of agentic frameworks
Agentic frameworks have several key features that distinguish them from other AI models and traditional AI systems. Their unique capabilities give organizations the building blocks to more easily create AI agents that boast autonomous functionality across their entire digital ecosystem.
Pre-built components
Agentic frameworks come pre-formatted with various components that support intelligent automation. These components define triggers and actions that drive each AI agent’s behavior.
Examples include predefined workflows, real-time data pattern recognition, and integrations with different applications and services. Pre-built components allow a templated approach to building and deploying new AI agents.
Communication protocols
Agentic frameworks support dynamic AI applications through specific communication protocols that AI agents follow to ensure task completion. Seamless collaboration between agents is essential for handling complex tasks.
Instead of one agent doing everything, agentic frameworks use orchestrators to assign different tasks to specialized agents, reducing errors and boosting performance. For example, one agent might create email accounts while another handles software licenses, avoiding common mistakes like incorrect permissions or missed installations.
Planning and reasoning
Agentic frameworks use reasoning engines to add intelligence to AI automation. Using a combination of large language models (LLMs) and extensive datasets, these AI agents analyze information, create goals, and plan different ways to achieve them.
This capacity to plan and reason about numerous automation tasks makes agentic AI incredibly dynamic. Unlike traditional AI, which might follow pre-programmed instructions, agentic AI solutions can perform more complex tasks while adapting to diverse scenarios.
For example, an agentic AI solution in enterprise IT support could help handle a VPN request by analyzing the employee’s role, checking security policies, planning approval sequences, and adjusting processes when additional reviews are needed. This adaptability ensures that the solution can handle both routine and exceptional cases efficiently.
Monitoring and debugging
The resilient design of agentic AI frameworks includes various monitoring and debugging tools. These tools enable AI agents to continuously track and enhance their performance, helping to avoid operational errors and eliminate time-consuming automation bottlenecks.
For example, in a procurement system, AI might notice delays in vendor approvals when certain documents are missing. The system can then learn to check for these documents earlier, reducing delays and increasing efficiency.
If developers need to make changes to an agentic AI system, the real-time data from the AI helps them quickly find and fix issues, ensuring the AI runs smoothly and reliably.
Why agentic frameworks are important
By design, agentic AI is more autonomous than traditional AI and is highly scalable. This scalability lets businesses quickly add multiple AI agents to handle increasing workloads efficiently. However, an agentic framework is essential to support this scalability.
Agentic frameworks offer the necessary structure and guidelines to deploy intelligent AI agents when needed. These frameworks define each AI agent's core capabilities and behaviors, making it easier and more consistent to replicate their design compared to starting from scratch.
Without an agentic framework, development teams would need to build each AI agent from scratch, programming their capabilities and monitoring their performance one by one. This process takes a significant time and resources. An agentic framework saves time and effort by offering a ready-made template for AI agents.
For example, if a company needs to add more customer support AI agents, an agentic framework allows them to quickly deploy many agents with the same high quality, instead of developing each one individually.
This is why agentic frameworks are crucial for easily scaling AI solutions, ensuring consistency, and saving valuable resources.
7 agentic AI frameworks
Agentic frameworks come in various shapes and sizes, so you’ll want to thoroughly research your options. It’s critical to understand the unique capacities and ideal use cases for each to select the best solution for your needs. Below, we'll review seven of the most popular frameworks and some of their key features.
1. LangGraph
LangGraph uses LLM applications to create an easy-to-use, graph-based approach for building agentic AI workflows. LangGraph supports the implementation of agent loops and leverages LangChain, a framework for developing applications powered by language models. It helps connect LLMs to other data sources and AI-powered apps for improved capabilities.
Key features
- Building applications that use LLMs for tasks like question answering over documents, summarizing content, creating chatbots, etc.
- Supports using cyclical graphs and branching to provide more dynamic control over agents
- Provides built-in state persistence to improve traceability and debugging
- Enables easy human oversight for approving complex workflows, modifying approaches, or rerouting agents
2. CrewAI
CrewAI is an open-source agentic framework that simplifies the orchestration of autonomous agents. It’s designed to orchestrate LLMs and other tools into "crews" or teams of agents. It focuses on simplifying the development of multi-agent systems powered by LLMs. This includes no-code deployments and compatibility with the LangChain ecosystem.
Key features
- Building complex applications where LLMs need to work together, leveraging different specialized capabilities
- Creates specialized AI agent teams to maximize efficiency and minimize redundancies
- Boasts an adaptive infrastructure that allows self-hosted or cloud-based deployments
3. Swarm
Swarm is an agentic framework developed by OpenAI. Its minimalist design features two primary core functionalities—agents and handoffs—as part of its structure. This design allows the self-contained units to perform their tasks within an agentic system while handing off or transferring functions to and from other agents based on the assignment context.
Key features
- Lightweight design that provides developers with a high level of automation control
- Can interpret easy-to-understand instructions using Natural Language Processing (NLP)
- Allows for building and testing individual agents in isolated environments before entering a larger swarm
4. ARCADE
ARCADE is a platform for developing and deploying multi-agent systems with a focus on reactive agents (agents that respond to their environment). Ideal applications are where agents need to react quickly and dynamically to changes in their environment. Examples include robotics control, traffic management, or real-time simulations.
Key features
- Offers a structured way to describe architecture with guidelines and a reference model
- Supports documentation and customization for specific use cases
- Adaptable to various software systems and technologies
5. FIPA
FIPA is a standards organization that widely influences how agents are built. It was established in the 1990s to address many of the traditional challenges in deploying software, specifically interoperability and standardized development practices. FIPA's Agent Communication Language (FIPA-ACL) and Agent Management System (AMS) are widely recognized and used in many multi-agent systems today.
Key features
- Provides a structured format for AI agent communication
- Offers standardized practices for managing agents throughout their lifecycle
- Allows for multiple agent types using different programming languages and build structures to function on the unified FIPA-OS framework
6. JADE
Java Agent Development (JADE) framework is a popular implementation of FIPA standards. JADE is regularly used in various industries, including robotics and logistics. It leverages Java programming language to build and deploy multi-agent systems, enable rule-based reasoning capabilities and more intelligent decision-making processes.
Key features
- Ensures different agent systems can communicate seamlessly using FIPA's common language and protocols
- Provides Java-based libraries and active forums for streamlined development and troubleshooting
- Uses an asynchronous message-passing model for smooth agent communication
- Distributed under LGPL, allowing free use and modification
7. LLaMA
LLaMA is a foundational LLM model developed by Meta. While it’s not considered an agentic framework, LLaMA serves as a starting point for fine-tuning for specific applications like text generation, translation, question answering, etc. Its open nature allows researchers to explore its architecture and capabilities.
Key features
- Provides access to a wide range of written languages to allow for cross-lingual data retrieval
- Compatible with LLaMA Stack, a large set of pre-configured tools and APIs to simplify AI development
- Allows for multiple model sizes to accommodate different resource constraints or performance requirements
How to choose an agentic framework
Not all businesses will decide to use the same agentic framework. Each option has pros and cons and may be better suited for specific industries than others.
Below are a few important things to consider when choosing your agentic framework:
- Define your business needs: Take the time to identify your unique business requirements. These include incorporating customer support chatbots, optimizing supply chains, or improving digital products and services.
- Outline specific objectives: Build out measurable goals that you can use to track the performance of your solutions. By clearly outlining objectives, you will be better prepared to find an appropriate solution.
- Look for tools and support: Consider whether the framework provides the necessary tools and libraries for your specific use case. Consider factors like data connectors, machine learning integrations, and debugging solutions.
- Think about your compatibility needs: Make sure the framework integrates seamlessly with your existing systems and infrastructure. A smooth integration will make building, monitoring, and maintaining a multi-agent system easier.
- Test, iterate, and define: Start with a small pilot project to test the chosen framework in a real-world environment. This will allow you to test the viability of your solution and ensure it can scale with your needs.
What’s the difference between AI agent builders and agentic frameworks?
While AI agent builders and agentic frameworks have a close relationship, it's important to understand their differences. Agentic frameworks are for building the foundation for complex AI systems, while AI agent builders are made to create and deploy AI agents more easily.
Agentic frameworks: Build the foundation for complex AI systems
Provide the foundational tools and infrastructure for building systems where multiple AI agents (or other software components) can interact and collaborate.
They contain the core architecture necessary for intelligent agents to interact in their environments and provide the blueprint for their operations. They often require programming skills and a good understanding of distributed systems concepts.
Agent builders : Create and deploy AI agents more easily
Offer a more user-friendly environment for creating and deploying AI agents, often with a focus on specific use cases. Agent builders like Microsoft Bot Framework, Google Dialogflow, or Moveworks Creator Studio integrate tools within an agentic framework that allow developers to create and interact with AI agents.
Agent builders can range from no-code platforms for non-programmers to more advanced tools, enabling developers to design, train, test, and deploy agents based on the framework principles.
Moveworks Creator Studio
Moveworks Creator Studio makes it easy to build, manage, and scale AI agents without heavy coding. Unlike traditional integration platforms, like iPaaS and other middleware, it uses an intelligent agentic Automation Engine to simplify the building, management, and scaling of AI agents.
It enables builders to manage their automation needs from one unified platform with minimal coding required.
Key features:
- Enables AI agents to pick the best action by analyzing and adjusting prompts dynamically
- Built to connect language to API values, allowing developers to build integrations without manual text parsing
- The Action Orchestrator executes actions visibly and reliably, handling dialogs, edge cases, and errors effectively
- A single, connected platform for developers to create and manage AI agents across diverse systems and an ever-growing library of plugins
Quickly build and deploy AI agents with Moveworks
Creating and launching your own AI agents can give your business incredible flexibility to automate and streamline your operations. But selecting the right agentic platform is critical for ensuring these successful business automation deployments.
With Moveworks Creator Studio, you can quickly build and deploy AI agents with just a tiny fraction of the typically necessary code. The platform is designed to help you:
- Automate repetitive tasks like IT ticket resolution, HR inquiries, and customer support issues
- Seamlessly integrate AI agents into your existing enterprise systems to eliminate inefficiencies
- Deploy multi-step automation across multiple teams, reducing the need for manual intervention
- Continuously improve workflows by leveraging AI’s ability to learn and optimize processes over time
Moveworks helps enable your teams to focus on high-impact work – by automating repetitive tasks and integrating seamlessly with your existing systems.
Want to take AI automation even further? Take our 2 minute quiz and get a personalized AI agent roadmap for your business.
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