At their core, AI agents are LLMs with a specific role and task that have access to memory and external tools. They use reasoning capabilities to plan steps and take actions to complete tasks.

Four components that make an agent “agentic”:
• An LLM (with a defined role and task)
• Memory systems (both short-term and long-term)
• Planning capabilities (to determine required steps)
• Tools (like databases, web search, or APIs)

𝗦𝗶𝗻𝗴𝗹𝗲-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀:
In its simplest form, a single-agent RAG architecture functions as a router. This approach can combine reasoning, retrieval, and answer generation in one agent.

𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀:
These systems chain multiple specialized agents together, often with a master agent coordinating the process. For example:
• One agent might intelligently retrieve information from various internal data sources
• Another could access, augment and clean the data
• A third might specialize returning personalized results to a user

𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗲𝘃𝗲𝗻𝘁 to learn how to build a system with these three types of agents: https://lnkd.in/ebQpsY3Y

Agents exist on a spectrum of autonomy. The level of “agentic” behavior largely depends on how much decision-making authority is delegated to the LLMs.

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An Agentic AI Stack is essential for creating intelligent, autonomous systems capable of handling complex tasks and making informed decisions.

The Agentic AI Stack shown below is a comprehensive framework designed to enable autonomous decision-making and task execution within AI-driven systems. It consists of multiple layers, each serving a distinct function to support the system’s operations.

At the foundation is the Tool/Retrieval Layer, which handles information gathering through web searches, APIs, operational databases, and SaaS platforms. It also includes vector databases, knowledge bases, business logic, and user interaction interfaces, ensuring the system can access and utilize diverse data sources effectively.

The Action/Orchestration Layer manages task execution with components like task management systems, persistent memory, automation scripts, and event logging, enabling the system to perform actions autonomously and maintain operational records.

Central to the stack is the Reasoning Layer, powered by Large Language Models (LLMs) and supported by contextual analysis tools, decision trees, and natural language understanding (NLU) components. This layer provides the cognitive capabilities necessary for understanding and generating human-like text.

The Feedback/Learning Layer focuses on continuous improvement through user feedback collection, model training, and performance monitoring, ensuring the system adapts and improves over time.

Finally, the Security/Compliance Layer ensures secure and compliant operations with data encryption, access control, compliance monitoring, and audit trails.

Together, these layers form a robust Agentic AI Stack, facilitating autonomous, adaptive, and secure AI system operations.

[Note: These are not the standard set of tools to be used. The tools and frameworks can change according to the use case]

Here is my comprehensive guide on building Multi AI agent systems: https://lnkd.in/g7wSCy9X

Here is my other article that talks about how you can build powerful AI agents using LangChain: https://lnkd.in/gfNs2S9i

This is my comprehensive guide to understand how to build agentic systems: https://lnkd.in/gc9pRqAA

Follow my Youtube channel to learn more about different AI frameworks: https://lnkd.in/gMCpfMKh

Also, having a robust data platform for your agentic application is a must, I recommend using SingleStore as your data platform for all your AI and RAG applications.

Try SingleStore for FREE: https://lnkd.in/gkprNvdN

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