AI Agents, Explained: How Walrus Enables the Next Generation of Autonomous Intelligence

We've all experienced the frustration of asking an AI assistant to handle what seems like a straightforward task. Maybe you need to find all the spreadsheets with sales metrics from 2024, compile it, and compare it to 2025 performance. Easy... right?

An AI can likely find the right documents - along with a few incorrect ones - but the actual work of reading files, compiling data, analyzing trends, and generating insights? That's still on you.

But AI agents are different. They don't just fetch information: they complete complex, multi-step tasks. These autonomous systems can work across multiple data sources and platforms, analyze patterns, synthesize information with context, and deliver finished work without requiring you to manually connect the dots.

That’s great, of course, but here’s the challenge: AI agents need a lot of data to function. From training datasets to memories from past interactions, more complex agents can’t perform properly without large amounts of data. And when AI agents operate autonomously, "sort of reliable" data isn't good enough. The last thing you want an agent to do is waste days of work only to draw the wrong conclusions. 

That’s why a data layer like Walrus is key to building the next generation of AI agents.In this piece, we’ll go into what an AI agent is, discuss a few real-world examples, and explore a few of the organizations using Walrus to power their AI agents today.

What is an AI agent?

To put it simply, an AI agent is software that can plan and complete multiple steps by itself. Instead of being told how to get to the outcome, it can reason independently, enabling it to complete more complex tasks. 

You’ve probably used an LLM before, which is reactive: you give it a prompt, it reacts and then stops. An AI agent goes one step further by proactively pursuing the objective it’s been given, including making decisions about the best steps forward, and then executing the processes it invented. 

Most AI agents have the following qualities:

‍Autonomous
‍You can set a goal, walk away, and expect to find it solved without any additional input from you.

Goal oriented
‍AI agents can use reasoning, planning, and memory to execute on objectives and complete tasks.

Adaptive learning
‍AI agents can learn from their experiences over time, improving their performance on tasks. 

Together, these characteristics enable AI agents to handle complex, multi-step workflows.

AI Agents in the Real World

AI agents may sound theoretical, but developers are already actively building agents that handle real tasks in different industries. 

Finance
‍AI agents can monitor markets, looking for opportunities, and make a good trade when one comes up. They can also learn from past trades and the latest market information to improve over time.

Customer service
‍What if the next customer service rep you got actually remembered your history and preferences? AI agents can do more than just spit out prewritten responses - they can respond to current issues based on past conversations you’ve had.

Content moderation
‍Sifting through content online to keep communities safe is tedious work for a person. An AI agent can do all that and more, responding by evolving policies based on community or worldwide events. 

What all these use cases have in common is the need for a reliable, scalable data layer. Whether it's a trading agent analyzing market patterns or a customer service agent remembering past conversations, they all depend on constant access to trustworthy data. But here's the challenge: traditional Web2 infrastructure wasn't built for autonomous systems that need data to be always available, infinitely scalable, and provably unbiased and accurate. 

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Why AI Agents Need Decentralized Data

There’s a critical problem with building AI agents on traditional web2 infrastructure: trust. Right now, AI agents rely on centralized cloud storage, which creates several issues:

Single point of failure
‍If the cloud service goes down, your agent goes down too. Not great for something expected to operate autonomously and around the clock.

Less control
‍You don’t actually control where your data lives or who can access it. You have to trust that the cloud provider’s policy will protect you. 

No proof of authenticity
‍The data an agent is trained on is critical for both performance and reliability. Centralized systems can’t offer proof that data hasn’t been tampered with. 

That’s where Walrus comes in.

Instead of storing data on one company’s servers, data is split up and distributed across multiple independent nodes. No single entity controls where your data lives or who can access it.

Here’s why that’s important for AI agents.

Always available
‍Your data isn’t dependent on one company’s uptime. Even if some nodes go offline, your agent can still access what it needs to get the job done. 

Fully verifiable
Every piece of data uploaded to Walrus gets an onchain proof of availability on Walrus. You can verify that data hasn’t been tampered with. 

Scalable
‍Walrus was designed to resist the natural drift towards centralization that happens as networks grow. Nodes are rewarded based on performance, not size, which keeps power distributed. 

When you’re trusting an AI agent to make autonomous decisions with potential real world impacts, decentralized data is more than a nice to have. It’s an essential part of your tech stack. 

That's exactly why developers are choosing Walrus as their data layer. From onchain trading agents to multi-agent collaboration platforms, teams are building the next generation of autonomous AI using Walrus to give their agents the reliability, verifiability, and scalability they need.

AI Agents Built on Walrus

Here's how different platforms are leveraging Walrus today to power their agents.

Talus: Onchain AI Agents

Talus is building a platform for launching AI agents that execute workflows directly on the Sui blockchain. With Walrus, Talus agents can access their data directly without moving it back and forth between cloud storage and blockchains. This means a DeFi agent can instantly grab the information it needs and execute a trade quickly, without delays. 

elizaOS: Multi-Agent Memory

elizaOS is a platform for building and orchestrating autonomous AI agents that work together as a team. They've integrated Walrus as their memory layer, enabling agents to remember past conversations, share information, and coordinate on projects. And because everything stored on Walrus has proof of availability, developers can also verify what their agents are doing. 

Zark Lab: AI Intelligence Layer

Zark Lab uses Walrus to help AI agents find and organize content. Zark’s AI automatically tags files so you can search using normal language instead of remembering specific file names or dates. Developers can automatically embed this intelligence into applications and programs stored on Walrus.

FLock.io: Decentralized AI Training

FLock.io empowers communities to train AI models together without sharing sensitive data. They then use Walrus to store and share the learning results across the network while keeping information sure. This means groups of people can come together and work collaboratively on AI models without putting their private data on a centralized server.

Conclusion

AI agents represent a fundamental shift from reactive AI tools, like ChatGPT and others, to autonomous systems that can truly work on our behalf. They don't just answer questions or retrieve information. They pursue goals, make decisions, learn, and execute workflows without constant human oversight.

But agents are only as capable as the infrastructure supporting them. Every AI agent needs somewhere to store training data, maintain memory across sessions, and access working information in real-time. When agents are making autonomous decisions with real consequences, that data needs to be trustworthy and provably available. Walrus answers this demand, providing a platform developers can trust to keep data safe, reliable, and always available. 

The next generation of AI agents is being built today. The data layer powering them will determine what they're capable of achieving.