What Makes An AI Agent Tick? - Quandatics Blogs

Quandatics | June 3, 2025

What Makes an
AI Agent Tick?

AI agents are no longer a futuristic concept—they’re already reshaping how modern businesses operate. Acting as digital labor, they perform tasks, make decisions, and assist humans with minimal intervention, while continuously self-improving.

According to Forbes and Salesforce, we’ve entered the third wave of AI — centered around Agentic AI. These systems don’t just support work; they execute it with intent, autonomy, and adaptability, all guided by a set objective. With adoption growing globally across sectors, embracing this shift is becoming essential to maintain a competitive edge.

But before jumping into adoption, it’s critical to understand how AI agents work. Knowing their inner mechanism, how they perceive, reason, act, and learn—helps set the right expectations and ensures your organization is equipped to deploy them effectively.

AI Agent as a Digital Labour

Before we explore the inner mechanisms, let’s start with a simple analogy. Think of an AI agent as a “virtual employee” or “digital labour”, operating behind the scenes to complete tasks and reach defined goals.

Just like a human, an AI agent is equipped with the following:

👁️ Eyes – Perception
Just as humans use senses to interpret the world, AI agents gather data through sensors, APIs, triggers, or user input. This is the first step in its workflow: understanding the environment or task context.

🧠 Brain – Decision making
At the heart of the agent is its reasoning brain—often powered by large language models (LLMs) or domain-specific algorithms. This system interprets the input and chooses what to do next based on goals, rules, and available data.

🗣️ Mouth – Communication
AI agents can’t contribute all by itself. They need to interact with tools, whether it’s reaching out to users, accessing external systems, pulling from a repository, or syncing with other agents. This is done through integrations, APIs, or messaging layers that allow it to collaborate in real time.

🛠️ Hands & Legs – Actions
Once a decision is made, the agent executes. This could mean triggering workflows, submitting forms, updating records, or activating third-party tools. It’s the “doing” part of the loop.

📚Memory & Learning – Self-Learning
The best agents get better with experience. Through feedback loops, performance tracking, and learning models (like reinforcement learning), AI agents adapt their behavior and fine-tune their responses for smarter outcomes.

Mechanisms Behind The Analogy

Eyes (User Interface)

Captures user queries and displays insights
Acts as the front-end touchpoint where humans interact with the AI system
Receives input and visualizes output via app hosting platforms

Brain (LLM / Core Intelligence)

Processes input using large language models like ChatGPT, LLaMA, Mistral, DeepSeek
Plans tasks, reasons through logic, and retrieves relevant knowledge
Drives task execution with autonomy and optimization logic

Mouth (API / Communication)

Facilitates interaction between AI agent and external systems or tools
Sends instructions, retrieves updates, and maintains workflow continuity

Hands & Legs (Tools / Actuation Layer)

Executes real-world actions like sending emails, scheduling tasks, generating reports, etc.
Performs the actual work based on the AI agent’s instructions
Automates routine or repetitive tasks, increasing efficiency

Memory & Learnings (Database / Knowledge Base)

Stores structured & unstructured data (PDFs, documents, databases)
Uses vector databases and embeddings to retain context and enable retrieval
Powers the learning loop by feeding data back into the system for better performance over time

This translates into increased productivity, reduced operational costs, and smarter, faster responses in business processes. Agentic AI isn’t just a tool — it’s a workforce transformation.

Disclaimer: The workflow and mechanisms described above represent a widely adopted conceptual framework for AI agents. The tools, models, and platforms mentioned (e.g., LLMs, APIs, feedback systems) are provided purely as examples and are not exhaustive or prescriptive. Depending on the complexity or specificity of a use case, alternative or customized architectures may be required.

Conclusion

Building and deploying AI agents is a complex initiative that involves strategic design, thoughtful customization, and rigorous testing to ensure reliable outcomes. By understanding the human-like components behind AI agents—how they perceive, think, act, communicate, and learn—businesses can enter this journey with realistic expectations and better control over the implementation process.

This foundational knowledge equips organizations to architect AI systems that do more than automate—they optimize operations, scale intelligently, and respond dynamically to change. While the setup may demand investment in time and effort, the long-term impact of agentic AI is transformational—streamlining workflows, enhancing decision-making, and enabling a new standard of digital labor across industries.

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