#3: The four agents of Agentic AI: driving the future of autonomy [3-min read]
Exploring #FrontierAISecurity via #GenerativeAI, #Cybersecurity, #AgenticAI.
AI Security Chronicles: Innovating with Integrity @AIwithKT
"The future is about pairing the rise of artificial intelligence with human insights to solve bigger challenges."
— Ginni Rometty
AI agents are the backbone of Agentic AI architecture. At its core, Agentic AI is about integrating many AI agents together — each designed for a different purpose — seamlessly, to create an autonomous, multi-step, end-to-end (enterprise) decision-maker.
Different types of AI Agents
Agent Assist: These agents excel at handling day-to-day tasks efficiently through human-AI collaboration. They analyze interactions in real time, identify customer pain points and suggest relevant responses. Some agents aggregate and deliver information from a variety of sources, making them ideal for less-regulated and more dynamic environments. Others are designed specifically for strict compliance frameworks, ensuring every action adheres to rigorous standards.
Workflow-Focused Agents: Masters of automation, these agents intelligently generate and execute workflows across different applications. They can autonomously identify the right APIs for various purposes, determine optimal sequences and fulfill user requests with precision.
The role of agentic orchestration
The core of an Agentic AI system lies in its ability to orchestrate diverse agents. The architecture enables the entire system to integrate seamlessly as a single, unified organism while also allowing logical domains to group similar agents. This logical grouping simplifies execution, deployment and management for different teams within an organization.
This design ensures that while each team operates with independent goals and tasks, they align with the overall AI architecture, amplifying enterprise solutions across the board. This orchestration transforms what could be siloed functions into a harmonious and efficient ecosystem.
Integrating external agents
In the previous post, we talked about the “Act” step (Step 3) of the Agentic AI process. This step involves integrating external data sources, tools and software through program interfaces. A remarkable feature of AI agents is their ability to incorporate external agents not originally built on the same platform. This capability minimizes tech debt and allows organizations to quickly integrate new and legacy technologies without disrupting existing workflows, architectures, or systems.
This adaptability is crucial for companies striving to innovate rapidly while maintaining operational continuity.
The four core agent types in Agentic AI
Generative Information Retrieval Agents: Designed for less-regulated environments, these agents excel at aggregating and outputting knowledge.
Prescriptive Knowledge Agents: Crafted for highly-regulated environments, these agents provide knowledge outputs that adhere to strict compliance standards.
Dynamic Workflow Agents: Action-oriented agents that autonomously generate and execute workflows (we’ll explore these in more detail another time).
User Assistant Agents: Focused on helping users directly by managing and automating day-to-day tasks autonomously.
Agentic AI is a dynamic and versatile architecture. The diversity of its agents and their ability to integrate seamlessly into existing systems make it a powerful tool for solving real-world challenges across industries. As we continue to explore the potential of these agents, I can’t help but wonder: What will be the next breakthrough in integrating agents to further streamline enterprise operations?
Innovating with integrity,
@AIwithKT 🤖🧠