You've probably have interacted with different kinds of AI platforms. These platforms, like ChatGPT or Gemini, are what we call a Large Language Model, incredible at understanding and generating human-like text, answering questions, and even writing creative content.
These LLMs are good at responding to your prompts, but you will need to be present for them to be useful. They are not autonomous, and need constant human intervention to work.
Agentic AI is a concept that evolves from the need of a more independent AI: the one that could proactively work towards a goal, remember past interactions, learn from its mistakes, and even use external tools, without needing a human to tell it what to do all the time.
What Exactly is Agentic AI?
An agentic AI system is designed to act autonomously to achieve a specific objective. It’s not just about generating text; it's about perceiving, planning, acting, and learning within an environment to reach a goal.
Here’s the simplest way to put it: Large Language Models (LLMs) are the cognitive engine or the "brain" of Agentic AI, but Agentic AI is the entire "body" and "nervous system" that puts that brain into action.
Traditional LLMs:
Reactive: They respond to your prompts. You ask a question, they give an answer.
Stateless: They don't inherently remember previous interactions unless specifically programmed to maintain a very short conversational context. Each new prompt is often a fresh start.
Limited Autonomy: They don't initiate actions or decide to use external tools on their own. They wait for your command.
Focus: Primarily on text generation, summarization, translation, and understanding.
Agentic AI:
Proactive & Autonomous: It can set its own sub-goals and take initiative to accomplish a larger objective.
Stateful (with Memory): It maintains persistent memory, allowing it to learn and adapt based on past experiences and ongoing tasks.
Tool-Using: It can decide which external tools (like a web browser, a calendar app, or a database) to use and how to use them to achieve its goals.
Focus: On executing complex, multi-step tasks autonomously by leveraging LLMs as their reasoning core and integrating other components like memory, planning, and tool orchestration.
In essence, Agentic AI builds upon the powerful language understanding and generation capabilities of LLMs, extending them with the ability to reason, plan, act, and remember, turning them into truly autonomous and goal-driven systems. And when we are talking about Protos AI, we are talking about an Agentic AI will all of these capabilities but tailored to Cyber Risk and Threat Management and Cyber Insurance.
How Protos AI Can Revolutionize Cyber Risk and Threat Management
The proactive, autonomous nature of Agentic AI makes it incredibly valuable for the fast-paced world of cybersecurity. It can act as an ever-vigilant, highly efficient digital security analyst, working tirelessly to protect your systems.
Here's how Protos AI can support cyber risk and threat management:
By using Protos AI, organizations can scale their protection, reduce human workload, make faster and more consistent security decisions, and move from a reactive security posture to a proactive and even anticipatory one. Protos AI empowers security teams to focus on more complex, strategic challenges, making our digital world a safer place.