AI agents – also called “agentic AI” – are systems designed to act on your behalf, not just give you information.
Unlike traditional AI tools that simply respond to prompts, AI agents can take initiative, carry out tasks over time, and make decisions to reach a goal. Think of them more like digital assistants that can do things, not just say things.
A Simple Example
Imagine you ask a typical AI to help you find the latest patents in battery technology. It might return a list and stop there.
Patsnap AI agents, on the other hand, can:
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Search for the latest patents
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Compare them to a list of competitors you care about
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Summarise trends
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Schedule a report to be sent to your inbox every week
That’s because AI agents are designed to follow objectives, use tools, and work through steps—like a human assistant would.
How They Work
AI agents combine a few key elements:
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Goals – You give it a target or objective.
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Memory – It can recall past actions or information.
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Tools – It can use apps, data, or services to complete tasks.
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Planning – It can figure out what steps to take to meet the goal.
Why It Matters
Patsnap Agentic AI can save time by automating complex workflows, following up on tasks, and running in the background. That means less manual searching and more automated insights tailored to what you actually need.
Here's a handy table that lays out some of the core components of agentic AI:
|
Agentic AI |
Generative AI |
|
|
Core Functionality |
Emphasis on goals & action, capable of acting independently and intelligently to accomplish tasks. |
Emphasis on insights, using LLMs to generate new information based on the data it has been trained on. |
|
Autonomy |
Operates with less human supervision. |
Requires human guidance to determine the context and goals of its output. |
|
Interaction with Environment |
Has mechanisms for perception, decision-making, and action – can use sensory data and machine learning models (e.g., computer vision, NLP) to understand the environment and adapt to fluctuations in energy demand, for example, to optimize energy consumption. |
Does not typically interact with its environment. Context is provided as part of training or coupled as part of a prompt. |
| Applications |
Robotics, self-driving vehicles, autonomous trading, smart grid optimization |
ChatGPT (chatbot), Stable Diffusion (image generator), GitHub Copilot (coding tool) |
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