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If you want your business to stay competitive, consider AI-led automation to streamline your operations. You’ll find agentic AI can take over a lot of your processes, giving your teams the space for strategic thinking and planning.
If you’re thinking about going down that path, we’ve created this resource to help you learn more about AI agents and identify the best framework for your needs.
What Is Agentic AI?
Traditional artificial intelligence was simple and rules-bound. It could automate repetitive tasks, but only because it had been given specific instructions on what to do and how to do it. It couldn’t make its own decisions.
Then came generative AI, or GenAI. It could produce results that didn’t strictly adhere to rules. Plus, technologies like natural language processing (NLP) almost made it sound like a person. However, even though it sounded like a human being, it could only give outputs based on what it had been taught. If the answers to the question were outside the scope of its training data, you’d be very likely to get “hallucinations” or made-up answers. It required prompts from users to respond and was incapable of independent action.
Agentic AI is the next generation of AI technology, where the models combine the ability of both traditional and generative systems and take them a step further. It can complete tasks without human supervision and make complex decisions. Most importantly, it can learn from the feedback provided as well as by directly interacting with its environment.
What Is an AI Agent Framework?
An AI agentic framework is a software platform that is used to create, deploy, and manage AI agents. These frameworks provide your developers with pre-built components—i.e., building blocks—to make creating these autonomous systems easier. You can use their tools, libraries, and templates to put together the features you need and build your own AI agent without having to start from scratch.
Key Components of AI Agentic Frameworks
Typically, an AI agentic framework includes:
Agent Architecture
This might be considered the equivalent of a brain, as this is what controls how the agent thinks, decides, and remembers. It typically includes decision-making processes, memory storage, and rules for interactions.
Environment Interfaces
The interfaces can be web browsers, databases, sensors, or APIs, and they determine how the agent interacts with its environment, whether that’s the real world or a simulated one.
Task Management
This is similar to a to-do list for organizing, assigning, and tracking tasks. The task management component is what enables the agent to break down complex tasks into smaller steps and assign them a priority ranking.
Communication Protocols
These methods are how the agent can interact, either with other agents or with humans in agentic systems. These typically include natural language processing (NLP), APIs, and structured messaging formats.
Learning Mechanisms
This component is the “trainer” or “teacher,” which helps the agent to learn from its past actions and eventually improve over time, using machine learning (ML), reinforcement learning, and user feedback.
Integration Tools
If you want your agent to deliver real-time services, it would need to be connected with external software, data sources, and APIs. These integration tools ensure that your AI agent has access to live information so it can make relevant and timely decisions.
Monitoring and Debugging
All technology must be monitored and updated to continue working as intended and to align with the latest developments. This is the component that enables performance tracking, error fixing, and behavior optimization. It usually includes tools for logging, analytics and reporting, and interfaces for debugging.
The Importance of AI Agent Frameworks
Now, why would one need AI agent frameworks at all? Here are some of the benefits of using one to develop your agent:
Quicker Development
Starting from the ground up takes longer than putting together pre-built components. With agentic AI frameworks, you can speed up your model development by simply putting together the components you need and configuring them. Just plug in the large language model (LLM), add memory storage, connect your APIs with RAW, and you’re ready to go.
Consistent Approach
Agentic AI frameworks give you a solid foundation to work from by making sure everyone on the team follows the same best practices. Instead of reinventing the wheel, your teams can build on standardized methods, which makes collaboration easier and ensures AI agents work reliably across different projects.
Scalability
As your business needs grow, you also want your agent and its capabilities to grow. Instead of having to add more features manually, frameworks make it easy for you to upgrade your agent. Whether you're building a simple AI assistant or a full-blown multi-agent system, you can rely on these frameworks to grow with you. They’re designed to easily handle everything from small, single-use cases to large-scale, complex environments.
Accessibility
You don’t need to be an AI expert to build an agent for your business. These frameworks make advanced AI development easier to grasp and more accessible to developers and researchers at all levels.
Innovation
By handling the basics, agentic AI frameworks give you more room to experiment, explore, and push AI to new limits—without getting bogged down in repetitive setup work.
Popular AI Agent Frameworks
Camel
This AI agent framework is ideal for scenarios where different AI agents need to work as a team to complete complex workflows, from research assistants to automated business processes.
CrewAI
CrewAI makes it easy to assign roles and responsibilities to different AI agents, so they can work together as a structured team to coordinate and execute tasks efficiently.
Flowise
Flowise is a low-code/no-code AI framework. It’s great for developers who want to integrate AI into their projects without writing extensive code. It offers an intuitive visual interface for designing agent interactions, automation, and integrations.
Hugging Face Transformers Agents 2.0
Hugging Face’s Transformers Agents take LLM-powered AI to the next level by enabling agents to browse the web, run Python code, and interact with APIs for real-world awareness.
LangChain
If you want to build an AI agent with reasoning capabilities, along with memory and retrieval, LangChain is the framework for you. It makes it easier for you to integrate LLMs, external tools, and APIs.
LangGraph
LangGraph builds on LangChain by adding a structured, graph-based approach to AI workflows, helping AI agents think through multi-step tasks, follow decision trees, and recursively solve problems.
MemGPT
Most AI systems forget past interactions. MemGPT, on the other hand, combines LLMs with efficient storage and retrieval methods to fit your AI agents with long-term memory, so they're able to retain and recall relevant details over time.
MetaGPT
This framework is designed to automate software development using AI. It organizes AI agents into a structured dev team, where one acts as a project manager, another as a coder, and yet another as a reviewer, and so forth. Together, they handle everything from project planning to code generation and debugging.
Microsoft Semantic Kernel
Microsoft Semantic Kernel makes it easier to integrate AI with traditional programming. It allows developers to use LLMs alongside standard programming logic. This framework would suit you if you need applications that need a mix of AI-driven reasoning and traditional software engineering workflows.
Microsoft AutoGen
AutoGen enables multi-agent collaboration, where they interact dynamically and solve complex tasks together. It’s particularly useful for applications where agents need to negotiate, coordinate, or share knowledge.
OpenAGI
OpenAGI is a forward-thinking framework aimed at building general AI agents that can autonomously explore, learn, and adapt. It’s a step toward self-improving AI, where agents can understand new problems and acquire new skills without explicit programming.
Promptflow
Promptflow is a visual workflow builder that makes designing and optimizing AI interactions easier. Developers can test different prompts, fine-tune agent behaviors, and create structured workflows, making it a powerful tool for LLM-based applications and AI automation pipelines.
PromptLayer Workflows
PromptLayer adds version control and tracking to AI prompts, which allows developers to monitor, optimize, and iterate on AI-generated responses. If you’re working on LLM-powered applications and need a way to track performance improvements over time, this tool helps refine agent outputs.
Swarm by OpenAI
Swarm is designed for coordinated AI systems, where multiple AI agents work together, like swarm intelligence, to solve problems efficiently. This framework is ideal for applications that need distributed decision-making.
Powering AI Agents With APIs
As you might have figured out, building agentic AI requires APIs. If you want to connect your data to your AI agent, RAW can help. Build, host, deploy, and manage your APIs for powerful automation.
