Although AI technology has just entered our lives, it has already begun to develop rapidly and integrate into different areas of both daily and professional life. AI technology started with rule-based systems, continued its life as deep learning and generative AI, and today it has developed as AI agents that provide services by combining different AI models. The accelerating development of AI technology, which is still new, has led to new trends and the discovery of flexible uses of AI. If you want to learn the difference between two new AI technologies, agentic AI and non-agentic AI tools, we've got you covered!

In this article, we will examine the differences between agentic AI and non-agentic AI and explore their intended uses.

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TL; DR

  • Agentic AIs are AI systems that can autonomously execute complex and multi-step tasks and make independent decisions.
  • Non-agentic AIs are AIs that can process only a single task at a time and require human input or guidance to generate output.
  • The biggest difference between agentic AIs and non-agentic AIs is that agentic AIs can make autonomous and independent decisions, while non-agentic AIs require commands.
  • Agentic AIs offer their users a variety of AI models, such as image generators and LLMs, while non-agentic AIs generally offer only one AI model at a time.
  • Agentic AIs quickly integrate into organizations such as enterprises and companies and adapt to them with self-training, while non-agentic AIs are designed for generic use cases.
  • If you are looking for an AI assistant that can integrate with your enterprise data and automate your tasks, with its multiple LLMs, image generators, web search, knowledge bases, and powerful RAG, TextCortex is the way to go.

What is an Agentic AI?

Agentic AI refers to a new artificial intelligence technology that goes beyond simple task execution. Agentic AIs are designed for purposes such as being more autonomous, proactive, capable of making decisions to achieve complex tasks, and understanding the environment. Agentic AIs, unlike non-agentic AIs, have an independent decision-making mechanism and do not need human input and guidance to execute repetitive tasks. Once you set agentic AI to achieve an objective, it will analyze given data, gather information from the environment, create plans, roadmaps, and tasks, and take action to execute the process. Agentic AIs are capable of:

  • Set Goals
  • Plan and Strategize
  • Learn and Adapt
  • Interact

Defining Non-Agentic AI

Non-agent AI is the general name given to the most commonly used AI technologies today that require human input and guidance. While non-agent AI systems such as Generative AI are excellent tools for executing complex and specific tasks, unlike agentic AIs, they do not have automation or independent decision-making capabilities. Non-agent AIs operate within pre-defined rules and parameters to respond to inputs or generate outputs. Examples of non-agent AI include:

  • Text Generators
  • AI Chatbots
  • Image Generators
  • AI Search Engines
  • Voice Assistants

The Meaning of Agentic in AI

The word agentic used in Agentic AI comes from the concept of agency, the mechanism by which an individual (or in this case, an AI) can make independent decisions by observing the world around them. Agentic AI systems also use AI models and decision-making algorithms to achieve given objectives by observing the world around them. This process is close to the human decision-making mechanism and occurs autonomously.

Agentic AI vs Non-Agentic AI: Differences

Agentic AIs and non-agentic AIs differ in both their development purposes and areas of use. Agentic AIs are ideal for automating the workload of organizations, while non-agentic AIs are ideal for executing specific tasks. The main reason for this distinction is the differences between agentic AIs and non-agentic AIs. Let’s take a look at the differences between agentic AIs and non-agentic AIs.

The Way They Work

Non-agent AIs require specific inputs and human guidance to generate output. For example, to produce a surrealistic artwork using image generators that fall into the non-agent AI category, you need to enter prompts and trigger the AI. However, if agentic AIs need to generate images in the process of achieving the given objectives, they trigger the AI ​​image generator without waiting for human input and continue the process using the output. After giving objectives to agentic AIs, you do not need to be involved in the remaining process, they execute all tasks from generation to data analysis autonomously. The biggest difference between agentic AI and non-agentic AI is that agentic AIs execute the entire process and multiple tasks autonomously, while non-agentic AIs execute only one task at a time with human input.

Use Cases

While non-agent AIs offer high performance in single-stage specific use cases, agentic AIs also offer high performance for complex and long-term tasks with multistep. You can use non-agent AIs to generate perfect output for a single task with methods such as prompting. You can use agentic AIs to automate complex and long-term repetitive tasks using perfect outputs. Agentic AIs will train themselves according to the feedback of their outputs during the objective execution process and become more useful to the organization they are integrated with. Non-agentic AI tools will provide standard performance under any conditions unless they are specifically trained.

TextCortex – Enterprise AI Companion

If you are looking for a company AI assistant that can automate your tasks with multiple LLMs including GPT-4o, GPT-4o Mini, Claude 3.5 Sonnet, Claude 3.5 Haiku, multiple AI image generators like DALL-E and Stable Diffusion 3, web search, knowledge bases and 30,000+ integrations, then TextCortex is designed for you. TextCortex is designed to help its users complete specific tasks and automate complex tasks such as knowledge management, document creation, and data analysis.

AI Agent Use Case

With TextCortex, you can connect your entire data ecosystem with a single click and manage it from a single platform. Once you connect your data ecosystem with it, you can give objectives to TextCortex and let it automate the whole process for you. Thus, you can save time by automating your repetitive tasks and focusing your extra time on more critical aspects of your enterprise.

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Frequently Asked Questions

How is agentic AI different from traditional AI?

Traditional AIs generate output according to pre-defined rules and parameters and cannot adapt to today's complex and fast-changing work environments. Agentic AIs refer to AI technologies that adapt to their environment, continuously improve themselves, and can make independent decisions to generate output.

What is an agentic AI?

Agentic AI is an AI system that uses multiple AI models to automate complex goals and workflows, without requiring human input or guidance in the process. Agentic AIs offer autonomous decision-making, planning, and adaptive execution to complete multi-step processes.

What is the difference between GenAI and Agentic AI?

GenAI (also known as generative AI) requires user input to generate output and take action. Unlike GenAI, agentic AI can act independently to generate output, make decisions, and take action. While GenAI generally generates output in only one sort of data type, agentic AIs can generate data in any sort of type, including code, text, and images.