Agentic AI is the next frontier of Gen AI systems that autonomously make decisions and complete tasks. In the architecture of agentic AI, the core building units are the agents. Agentic AI involves multiple AI agents that coordinate and communicate through prompts, use various external tools, utilize memory to store past interactions, and take appropriate action.
Curious to know how agentic AI works? This blog explains the various phases, such as prompt, tools, memory, coordination, governance, and learning. So what’s holding you up?
Let’s dive in!xd
How Agentic AI works
Prompt
Prompting is the first step in initiating agentic AI action through natural language, like asking for help from a human agent. When a user gives a prompt such as, “I want to track my current order. If I didn’t receive it within 3 days, I would like to know the cancellation policy,” the perception module comes into action afterwards.
Think of a prompt as a trigger, activating the perception module and enabling agents to gather, analyze, and process information to guide further actions.
This module ensures AI agents comprehend different input types, such as visual, speech patterns, or other sensory information.
Did you know AI agents perform a sequence of parallel actions? The perception module enables the agent to break down a complex query and manage multiple-step tasks simultaneously from a single prompt.
This means that with one prompt, the AI agent can track the customer’s current order, check if it will be reached within 3 days, or retrieve information about the cancellation policy.
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Tools
AI agents are further moved to reasoning and planning using LLMs. They process the sensed information, draw inferences, and formulate plans. Additionally, AI agents leverage external tools beyond simple text generation, accessing real-time data, interacting with databases, or retrieving search results. These external tools include APIs, CRM systems, vector search engines, and bug-tracking software.
Consider tools as extensions that enhance AI agent reasoning and decision-making processes by interacting with external systems. Frameworks like LangChain facilitate interaction with LLMs and external tools, enabling more complex task execution.
LangChain: Facilitates building applications powered by LLMs. It integrates APIs, databases, and external systems for advanced reasoning and action.
Memory
An AI agent’s memory is an important component. Like humans, AI agents mainly rely on two core memory systems to maintain a knowledge repository. Short-term memory usually manages what is happening at the moment, such as prompt interaction, real-time conversations, current task details, or recently processed information.
Short-term memory is fast but limited. However, long-term memory stores everything, such as past interactions, learned processes, and more.
But what happens if both systems work together? It enables AI agents to access deep knowledge from the repository while staying present in real-time interaction with the user. This enhances the contextual awareness capabilities of AI Agents and delivers more accurate, informed responses.
Coordination
Each AI agent offers in-depth insights and actions while aligning with the collective goals. Shared memory facilitates coordination among these AI agents. It allows agents to exchange knowledge, track ongoing tasks, and align their actions. Additionally, it enables agents to maintain situational awareness and adapt to new inputs, ensuring consistent and informed decision-making across the system.
Governance
Agentic AI actions align with defined governance policies, ensuring compliance with legal and ethical frameworks. Agents adhere to these policies in a multi-agent system, facilitating functioning within regulatory standards while maintaining data security, trust, transparency, and integrity in AI-generated actions.
Learning and Adaptation
Learning and adapting are crucial aspects of AI agents, helping them enhance their existing capabilities and performance over time. AI agents incorporate feedback loops, which allow them to observe and analyze the outcomes of actions and share feedback with the system.
This feedback allows agents to update their knowledge and improvise reasoning and strategies. This learning process ensures that AI agents learn from their experiences, enhance their ability to take accurate action, and efficiently handle dynamic inputs.
Conclusion
These AI agents offer in-depth insights and actions while aligning with the collective goals. Shared memory supports coordination between these AI agents, enabling knowledge sharing among agents, monitoring active tasks, and synchronizing their actions. It also helps agents be situationally aware and responsive to new information so that decision-making throughout the system remains consistent and well-informed.
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