Building Web3 AI Agents: Challenges, Tools, and What You Need to Know

AI agents are changing how we interact with technology by autonomously handling complex tasks for us. When these intelligent systems interact with blockchains, they open up new opportunities, as well as unique infrastructural and design challenges.

In the first part of our series on AI agents, we discussed what they are and the components that make them work. This post focuses on building AI agents that can operate in web3 environments, where they need to manage cross-block state, securely sign transactions, and navigate the finality limits of decentralized networks.

Building Web3 AI Agents Is Different

AI agents aren’t traditional APIs. They don’t just wait for inputs and respond. They can plan, monitor, learn, and act on their own. An agent might automatically rebalance your DeFi portfolio when markets shift, log governance votes based on predefined criteria, or identify and claim airdrops on your behalf.

These agents are already being built. But their context-sensitive behavior introduces new technical demands when they interact with decentralized networks.

Here are four critical points you need to consider when designing agents for web3…

1. Agents Need to Keep State

AI agents require persistent memory. Agent runtimes operate across multiple steps and workflows, so they need to remember previous actions and maintain context between interactions.

Key takeaway: Stateless functions won’t cut it. You need infrastructure that supports stateful agents with long-term memory storage, session management, and continuity.

2. Agents Decide When and How to Act

AI agents can produce a range of reactive and proactive behaviors. They might:

Respond to external prompts (e.g., user input, API calls, onchain events)
Monitor blockchain events in real-time
Check conditions continuously
Schedule their own tasks
Orchestrate other agents to fulfill larger tasks

Key takeaway: You need a secure execution environment that handles everything from quick responses to long-running processes, and an event-driven execution layer that can manage onchain and offchain triggers.

3. Agent Communication Is Flexible

Rather than sticking to a consistent approach, AI agents can choose to interact with their environments in different ways. Depending on the use case and architecture, they might:

Make API calls
Trigger smart contracts
Run as containers
Pass messages through pub/sub channels or sockets

Key takeaway: You need adapter layers that normalize inputs and outputs across different protocols and frameworks (e.g., LangChain, AutoGen, CrewAI). Early standards like Model Context Protocol (MCP) aim for agents to share memory and context across tools.

4. Agent Outputs Must Be Trustworthy

AI agent outputs can be unpredictable and opaque. That’s acceptable in some cases, but not when they’re managing onchain assets or triggering high-value transactions. Web3 agents require additional safeguards.

Key takeaway: You need systems that include logging, optional user confirmations for critical actions, metadata that explains reasoning, and verifiable execution (e.g., EigenLayer-style proof frameworks).

Core Infrastructure Components for Web3 Agents

While every setup is different, a simplified architecture for AI agents might look like this:

In order to extend infrastructure to support AI agents natively in web3 environments, additional components are needed:

Execution Sandbox: A secure environment to host, isolate, and execute agent processes.

Session & State Management: A system to store and retrieve agent memory, track session context, and persist intermediate outputs.

Adapter Layer: An abstraction interface to normalize and translate between agent frameworks, blockchain protocols, and external APIs.

Event Bus / Pub-Sub System: Components that trigger agents based on onchain or API signals.

Job Queue & Scheduler: Management for retries, timed execution, and asynchronous agent tasks.

Audit Logging: Detailed records of agent actions for traceability, debugging, and potential compliance.

Tools and Frameworks for Building Web3 AI Agents

The web3 agent ecosystem is young but growing quickly. Several projects provide infrastructure and foundational toolkits for programmable agents that are worth investigating:

Eliza (elizaOS): An open-source, multi-agent framework for creating and managing AI agents, with web3-friendly architecture and LLM support.

Coinbase AgentKit: A toolkit from Coinbase Developer Platform that gives agents crypto wallets and enables onchain actions, compatible with different frameworks.

GOAT (Great Onchain Agent Toolkit): A toolkit focused on blockchain-native agent operations, with modules for wallets, minting, governance, and smart contract execution.

These frameworks aim to standardize and simplify the process of building secure, onchain-compatible agents.

Common Challenges

Given the inherent complexity of AI, blockchain, and decentralized coordination, it shouldn’t surprise you that building web3 AI agents isn’t plug-and-play. There are several recurring roadblocks.

Fragmented Tooling: You’ll often need to combine libraries and frameworks since mature, standardized tools for agent development are still emerging.

Security Risks: Agents can be compromised by adversarial inputs and context manipulation, or act on false assumptions, without continuous monitoring and hardened environments.

Scalability Constraints: As deployments and concurrent agent activities grow, you’ll need to ensure responsiveness on blockchain networks.

Regulatory Questions: Autonomous systems raise complicated questions around accountability, oversight, transparency, and traceability.

To learn more about designing AI agents for trustless execution in decentralized systems, read EigenLayer’s article: Introducing Verifiable Agents on EigenLayer.

Toward a Native Web3 Agent Layer

Bringing AI agents into web3 is about designing for persistence, autonomy, and trust from the ground up. To unlock their full potential, developers and no-code builders need to keep in mind stateful logic, flexible interaction models, and provable onchain actions.

A truly agent-native infrastructure that makes AI agents a core part of onchain systems doesn’t exist yet, but the pieces and platforms are taking shape. Ava Protocol is testing agent-compatible execution with event-driven workflows, privacy-preserving environments, and verifiable web3 automation.

In our next post, we’ll explore how Ava Protocol is extending its automation layer to support agentic workflows and enable new forms of AI-native execution.
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