AI Agents Explained: What They Are, How They Work, and Why They Matter

Chatbots are starting to feel outdated. A new wave of artificial intelligence systems are doing things like booking meetings, managing inboxes, and executing onchain transactions without human intervention. These AI agents don't just respond — they actually get things done for you.

This post is the first in a series that breaks down what AI agents are, how they operate, and what it takes to build AI agents effectively.

What’s an AI Agent vs. a Chatbot? Key Features

Chatbots communicate, answering your questions based on what they know. AI agents go further by acting on your behalf. They're like independent workers for your digital tasks. While an agent might chat with you to clarify things, its main purpose is to accomplish something for you. Give it an objective, and it figures out how to achieve it.

Autonomous AI agents can interpret context, decide what to do, plan the steps, and take action. Instead of waiting for your next prompt, they can proactively use tools like web browsers and APIs to get things done, whether it’s handling simple jobs or tackling complex problems using AI workflow automation.

Need to research market trends and get a report? An agent can browse the web, pull data from APIs, analyze it, and generate the output. Want to track real-time token prices and rebalance a DeFi portfolio? An agent can monitor price feeds, assess thresholds, and trigger trades onchain.

This ability to plan, use external tools, and execute tasks on their own is what distinguishes agent-based systems from conventional chatbots. Key categories include:

• Conversational Agents: Designed for dialogue-based interaction with users.
• Task-Execution Agents: Focused on completing defined tasks step by step.
• Event-Triggered Agents: Automatically activated by specific conditions to operate in the background.
• Multi-Agent Systems: Networks of specialized agents that coordinate to solve complex problems.

Building an AI Agent Is Like Hiring an Employee

When you bring a new worker into a business, you need to:

1. Explain their role and responsibilities.
2. Give them access to your systems.
3. Trust them to handle tasks independently.

When we build AI agents, it’s exactly the same — except these agents work 24/7, can be duplicated or modified instantly, and don’t take breaks.

Core Components of AI Agents

Let’s look under the hood. Just like humans need a brain, memory, and tools to do their job, AI agents rely on similar building blocks. AI agent architecture includes five core components:

1. Brain (LLM): A large language model, like GPT-4 or Claude, sits at the center. This is where reasoning, planning, and decision-making happen.
2. Prompting: You give the agent instructions by writing a prompt, which programs its actions without coding. This is part of what makes building agents so accessible.
3. Memory: Short-term and long-term memory help the agent stay consistent, keep track of current tasks, and learn from past experiences.
4. External Knowledge: You can give agents access to additional data and task-specific information, like product specs or customer info, that go beyond their original training.
5. Tools: APIs, webhooks, databases, and automation platforms enable agents to browse websites, access real-time data, use apps, and execute code.

Three Key Ingredients for Designing AI Agents

Although AI agents rely on five components, building AI agents typically comes down to three ingredients:

1. Prompting to define behavior.
2. External knowledge to provide context.
3. Tools to take action.

This trio establishes how agents behave, what they know, and what they can do. The underlying AI model itself — whether it’s ChatGPT, Claude, Gemini, or others — doesn’t really matter, since the capabilities of today's top models are fairly similar. Similarly, chat memory management is standard in most AI platforms.

What will truly differentiate an agent’s performance is how you approach prompt engineering, provide additional data, and equip it with necessary tools. Whether you’re running an AI agent that resolves support tickets or one that monitors blockchain events to trigger smart contract actions, these three elements are the foundation of the agent’s success.

How Agents Use Tools: Schemas Explained

It isn’t enough to give an agent access to tools. It also needs to know how to use them. That’s where AI agent schemas come in. You can think of a schema as an instruction manual for a specific tool. It tells the agent:

• What the tool does
• What inputs it needs
• What kind of output to expect.

Schemas for AI agents help them understand which tool to use for a specific step and how to use it. This enables it to reliably leverage AI agent tools, process inputs, interpret responses, and act. Sophisticated agents can also chain tools together, using one tool’s output as another’s input, and dynamically re-strategize their approach to a task when necessary.

AI Agent Use Cases

The possibilities for applying AI agents are expanding across virtually all industries. They include:

• Copilots for business (e.g., customer service and support)
• Personal assistants
• Deep research and analysis
• Lead generation and sales support
• Onchain agents that initiate token swaps or rebalance liquidity positions

Basically, any multi-step digital task that requires planning and interaction with different APIs, data sources, or smart contracts is a potential fit for an AI agent.

Getting Started with AI Agents

To recap: AI agents are more than just chatbots. They are autonomous systems with a brain, memory, and tools that allow them to understand goals, plan steps, and get things done. This enables AI agents to understand objectives, plan steps, and execute across both web2 and web3 environments.

Understanding basic components, prompt logic, task-relevant knowledge, and tool schemas is key to building effective agents. Agents are bridging the gap between information and action, reshaping how we handle and automate tasks, as well as how we interact with AI.

Stay tuned for our next post covering how to build web3 AI agents, followed by a third post about powering agentic workflows with verifiable triggers.
__

Join the Ava Protocol community on X, Discord, and Telegram.