Agentic AI refers to a type of artificial intelligence system that can operate autonomously to achieve specific goals. Unlike traditional AI, which typically follows pre-programmed instructions, agentic AI can perceive its environment, reason about complex situations, make decisions, and take actions with limited or no direct human intervention. These systems often leverage large language models (LLMs) and other AI capabilities to understand context, develop plans, and execute multi-step tasks.
An agentic AI toolset comprises the various software, frameworks, and platforms that enable developers and businesses to build and deploy these autonomous AI systems. These toolsets often include components that facilitate:

• Agent Creation and Configuration: Tools for defining the goals, instructions, and capabilities of individual AI agents. This might involve specifying the LLM to be used, providing initial prompts, and defining the agent’s role and responsibilities. Examples include the “Agents” feature in OpenAI’s new tools for building agents.
• Task Planning and Execution: Frameworks that allow agents to break down complex goals into smaller, manageable steps and execute them autonomously. This often involves reasoning, decision-making, and the ability to adapt plans based on the environment and feedback.
• Tool Integration: Mechanisms for AI agents to interact with external tools, APIs, and services to gather information, perform actions, and achieve their objectives. This can include accessing databases, sending emails, interacting with web applications, or controlling physical devices. Examples include the tool-use capabilities in OpenAI’s Assistants API and the integration capabilities of platforms like Moveworks.
• Multi-Agent Collaboration: Features that enable multiple AI agents to work together to solve complex problems. These frameworks facilitate communication, coordination, and the intelligent transfer of control between agents. Examples include Microsoft AutoGen and CrewAI.
• State Management and Workflows: Tools for managing the state of AI agent interactions and defining complex, stateful workflows. LangGraph is specifically designed for mastering such workflows.
• Safety and Control: Features for implementing guardrails and safety checks to ensure that AI agents operate responsibly and ethically. This includes input and output validation mechanisms.
• Monitoring and Observability: Tools for visualizing the execution of AI agents, debugging issues, and optimizing their performance. OpenAI’s new tools include tracing and observability features.
  Examples of Agentic AI Toolsets and Platforms (as of April 2025):
• Microsoft AutoGen: A framework designed for building applications that involve multiple AI agents that can converse and collaborate to solve tasks.
• LangChain: A popular framework for building AI-powered applications, offering components to create sophisticated AI agents with memory, tool use, and planning capabilities.
• LangGraph: Extends LangChain to build stateful, multi-actor AI workflows.
• Microsoft Semantic Kernel: A framework for integrating intelligent reasoning into software applications, enabling the creation of AI agents that can leverage plugins and skills.
• CrewAI: A framework focused on enabling AI teamwork, allowing developers to create teams of AI agents with specific roles and objectives.
• Moveworks: An enterprise-grade AI Assistant platform that uses agentic AI to automate employee support and complex workflows across various organizational systems.
• OpenAI Tools for Building Agents: A new set of APIs and tools, including the Responses API, Agents, Handoffs, and Guardrails, designed to simplify the development of agentic applications.
• Adept: Focuses on building AI agents capable of interacting with and automating tasks across various software applications through UI understanding and control.
• AutoGPT: An open-source AI platform that aims to create continuous AI agents capable of handling a wide range of tasks autonomously.
• AskUI: Provides tools for building AI agents that can interact with and automate tasks based on understanding user interfaces across different applications.
  These toolsets are rapidly evolving as the field of agentic AI advances, offering increasingly sophisticated capabilities for building autonomous and intelligent systems. They hold the potential to significantly impact various industries by automating complex tasks, enhancing productivity, and enabling new forms of human-AI collaboration.