Getting Started with AI Agents
Table of Contents
Introduction
Welcome to the world of AI Agents. This guide provides an overview of AI agents, the tools and infrastructure needed to build them, and examples to help get you started. Whether you’re starting with simple automation or building complex systems, this guide will help you navigate the ecosystem.
Why AI Agents?
AI Agents are making blockchain interactions more accessible and user friendly.
| Feature | Without AI Agents ❌ | With AI Agents ✅ |
|---|---|---|
| Easier Onchain Transactions | Users manually open wallets, find bridges, navigate menus, and execute swaps. | Users prompt AI Agents to swap tokens automatically. |
| 24/7 Participation in Global Markets | Users manually track DeFi yields and farming opportunities. | AI Agents operate nonstop, detect high-yield options, and reallocate funds. |
| Fewer Scams and Errors | Users are vulnerable to phishing attacks and human errors. | AI Agents detect scams, block malicious contracts, and prevent errors. |
What is an AI Agent?
AI Agents are software systems that perceive environments, make decisions, and take actions to achieve specific goals. Shaw (creator of Eliza/ai16z) describes them as "a new kind of website" offering interfaces that interact with APIs for solving complex tasks more effectively than traditional interfaces.
Key Characteristics:
- Goal-oriented behavior
- Environment perception and interaction
- Decision-making capabilities
- Ability to learn and adapt (in advanced implementations)
AI agents are particularly relevant to blockchain builders, as they simplify complex tasks like managing smart contracts, automating transactions, and interacting with decentralized protocols. They bridge the gap between blockchain infrastructure and intuitive user experiences.
Types of AI Agents
1. Basic AI Agents
- Simple tasks like chatbots with decision trees.
- Examples: Command processors, basic I/O automation.
2. Functional Agents
- Specialized agents with defined purposes.
- Examples: Code review agents, research assistants, or domain-specific solvers.
3. Autonomous Agents
- Operate with minimal human intervention.
- Capabilities: Multi-step reasoning, task automation, self-improvement.
4. Multi-Agent Systems
- Collaborative systems where agents work together.
- Examples: Collective problem-solving, distributed decision-making, or agent-to-agent communication.