AgentOS

The operating system for AI Agents
Built on RobinHood Chain

CA: 0x91fad9dade3fc557a74d46cf85e936f057ddebed

[AgentOS @AgentOS_RH]

AgentOS is live $AOS

The infrastructure for building, deploying and operating AI agents is now available.

Memory. Tools. Scheduling. Permissions. Analytics. Multi-model support.

Build the agent.

AgentOS handles the foundation.

CA: 0x91fad9dade3fc557a74d46cf85e936f057ddebed

More details coming soon!

[AgentOS @AgentOS_RH]

If your agent is doing real work, you need to know what it's actually costing you.

AgentOS tracks the infrastructure behind every agent:

→ Tokens
→ Cost
→ Latency
→ Errors
→ Tool usage
→ Success rate

Build. Deploy. Measure. Improve.
$AOS

[AgentOS @AgentOS_RH]

AGENTOS JUST ENTERED AiFi

AI agents are no longer limited to answering prompts or running simple tasks.

They can now be deployed to manage liquidity.

We're introducing Automated Liquidity Management for Agents a new protocol layer built into AgentOS that allows users to launch liquidity management agents directly from the dashboard.

The idea is simple:

-> Define the strategy.
-> Set the parameters.
-> Let the agent monitor and manage the liquidity.

These agents can continuously operate based on the rules and objectives defined by the user, removing the need to manually monitor positions and react to every market movement.

Think of it as:

Liquidity Strategy
↓
AgentOS
↓
Autonomous Liquidity Agent
↓
Continuous Monitoring & Execution

Instead of manually managing liquidity across the clock, users can deploy an agent designed to do the work. This is where we think the next evolution of onchain agents starts.

Agents shouldn't just be able to think. They should be able to monitor, decide and execute within the rules and guardrails you define.

And with Automated Liquidity Management now built into AgentOS, anyone can launch their own liquidity management agent directly from the dashboard.

Welcome to AiFi. Welcome to $AOS

More to come. 👀

[AgentOS @AgentOS_RH]

The interesting part about autonomous agents isn't that they can answer you instantly.

It's that they can keep working after you leave.

Research overnight.
Monitor something continuously.
Run scheduled workflows.
Complete tasks.

Wake you when something needs your attention. That's what autonomy looks like.
$AOS

[AgentOS @AgentOS_RH]

Why build your entire agent around one model?

AgentOS gives developers one interface across multiple AI providers.

-> OpenAI
-> Anthropic
-> Google
-> DeepSeek
-> Mistral
And many more.

One agent infrastructure. Multiple models.
$AOS

[AgentOS @AgentOS_RH]

Together with Private Memory, Memory Activity and Memory Controls, we're building a complete memory layer for agents that need to operate beyond a single conversation.

The agent remembers.
AgentOS gives that memory structure.
$AOS

[AgentOS @AgentOS_RH]

AGENTOS MEMORY JUST GOT A MAJOR UPGRADE

Memory is what allows an AI agent to become more than just a model responding to the latest prompt.

But memory shouldn't be a black box.

Developers need to understand what their agents remember, organize that information, and control how memory is used across different contexts.

That's why we've expanded the AgentOS Memory layer.

Introducing:

→ MEMORY CENTER

A dedicated place to view and manage everything an agent remembers.

Developers can now explore stored memories, search through them, add new information, edit existing memories, and remove information that is no longer relevant.

Instead of memory existing somewhere in the background, it becomes something you can actually inspect and manage.

Memories can also be organized into meaningful categories such as:

• Facts
• Preferences
• Tasks
• Other agent-specific context

Because not everything an agent remembers should be treated the same way.

→ MEMORY SCOPE

Not all memory belongs in the same context.

AgentOS now supports different memory scopes, allowing developers to define whether information belongs to:

• The agent
• A specific user
• A specific session

An agent can maintain its own long-term context.

Each user can have their own preferences and information.

And individual sessions can maintain context relevant only to that specific interaction.

The bigger idea is simple:

Agent memory shouldn't just be conversation history.

It should be a structured, manageable layer of infrastructure that developers can build on.

Memory you can explore.
Memory you can organize.
Memory that understands context.

$AOS

[AgentOS @AgentOS_RH]

Your agent shouldn't need you to press "run."

Tell it when to work.

Every hour. Every day. Every week.
Or when something happens.

AgentOS gives agents a schedule and lets them keep working when you're not there.
$AOS

[AgentOS @AgentOS_RH]

Autonomy doesn't mean unlimited access.

An agent should know what it can do and you should decide what happens when it tries to do more.

Allow/Deny
Require approval

AgentOS puts permissions between your agent and the actions it takes.
$AOS

[AgentOS @AgentOS_RH]

🔐 PRIVATE MEMORY IS NOW LIVE ON AGENTOS

As agents become more capable, memory becomes one of the most important parts of the infrastructure.

But giving an agent memory also means giving developers control over what gets remembered, what stays private, and how that memory is managed.

We’ve upgraded the AgentOS memory layer with three new capabilities:

→ Private Memory

Developers can now explicitly flag individual memories as private.

During inference, private memories are filtered out for non-owners, keeping sensitive context separated from what an agent can expose or use outside its intended scope.

→ Memory Activity

Memory is no longer a black box.

AgentOS now maintains an activity trail for memory operations, including when memories are created, retrieved, updated, deleted or searched.

Developers can see what is happening with an agent’s memory through the Activity feed.

→ Memory Controls

Developers have direct control over memory at the agent level.

Memory can be completely enabled or disabled for an agent, and the new Clear All capability allows the agent’s stored memory to be completely reset when required.

The goal is simple:

Agents should be able to remember without developers losing control over what they remember.

Private by design.
Observable when it matters.
Controllable when you need it.

Memory is becoming foundational infrastructure for agents.

AgentOS is building that infrastructure with privacy and control built in from the start.

$AOS

[AgentOS @AgentOS_RH]

$AOS × $AUREON 🤝

AI agents need more than intelligence. They need the ability to act.

We’re partnering with @buildaureon to integrate its financial intelligence toolkit as a native toolset within AgentOS.

Think of it as:

Aureon’s financial intelligence → AgentOS tools → autonomous agents

Developers building on AgentOS will be able to equip their agents with Aureon’s portfolio intelligence and financial capabilities without having to build the underlying financial logic, monitoring and policy based restore layer from scratch.

Agents can tap into capabilities around portfolio monitoring, target allocation, drift detection and objective based restore through the tools available to them.

The bigger idea is simple:

Developers shouldn’t have to rebuild specialized infrastructure every time they build an agent.

Aureon brings the financial intelligence.

AgentOS provides the infrastructure for agents to use it.

Plug it in. Give the agent the capability. Let it work.

This is another step toward a world where developers can compose powerful agents from specialized, agent native tools.

And this is just the beginning.

More agent native tools coming soon. 👀

[AgentOS @AgentOS_RH]

An agent that can only talk is limited.

Give it tools, and it can act.

-> Search
-> GitHub
-> APIs
-> Databases
-> Email
-> Custom tools

AgentOS gives developers a way to connect agents to the systems they actually need to work with.

$AOS
Try it yourself: https://t.co/x4wHTUYywA

[AgentOS @AgentOS_RH]

The best agents shouldn't have to be introduced to you every time they wake up.

AgentOS gives agents persistent memory for:

→ conversations
→ preferences
→ facts
→ previous actions
→ context

So the agent doesn't just respond, it remembers.
$AOS

[AgentOS @AgentOS_RH]

AgentOS is now programmable beyond the dashboard.

We’ve shipped `@agentos/sdk` giving developers a direct interface to build, manage and integrate AgentOS agents into their own applications.

Two clients. Two layers of the platform.

AgentOSManagementClient

Manage your AgentOS infrastructure programmatically:

→ Create, update, clone, pause & resume agents
→ Configure models, prompts, tools, knowledge & agent settings
→ Manage public agents and versions
→ Create, list & revoke agent-scoped API keys
→ Manage chat sessions

Authentication is wallet-signature based. The SDK never touches your private key you sign with the wallet tooling you already use and provide the resulting signature.

AgentOSClient

Take a deployed agent and put it directly into your application:

→ Streaming chat over SSE
→ One-shot generation
→ Model selection within the agent's allowed models
→ Usage information
→ Abortable requests
→ Structured API errors

Getting started is simple:

npm install @agentos/sdk

1. Create your agent in the AgentOS dashboard.
2. Open the agent → API Keys → create a key.
3. Copy the `aak_...` key — it’s shown only once.
4. Store it securely and use it with `AgentOSClient`.

The key is scoped to that specific agent, and the caller doesn't need an AgentOS account or wallet.

API usage is billed to the agent owner's wallet.

This is an important step for AgentOS:

Agents don't have to live inside our dashboard.

Developers can build them on AgentOS and bring them into the applications they're creating.

Build the agent. Connect it anywhere.
$AOS

[AgentOS @AgentOS_RH]

A model can generate an answer.

An agent needs to do more.

It needs to remember context, use tools, follow instructions, take actions.

Model is the brain.
AgentOS provides the rest of the system.

$AOS

[AgentOS @AgentOS_RH]

@crypto_ideology Done

[AgentOS @AgentOS_RH]

AgentOS isn't just where you create an AI agent.

It's where you give it everything it needs to operate:

→ Memory to retain context
→ Tools to take action
→ Scheduling to work autonomously
→ Permissions to control what it can do
→ Analytics to measure how it performs
→ Multiple models to power its intelligence

One place to build, deploy and operate your agents.
$AOS
Try yourself: https://t.co/x4wHTUZ6m8

[AgentOS @AgentOS_RH]

A lot of the work behind AgentOS happens where you can't see it.

We're constantly improving the infrastructure, refining the developer experience, and pushing new capabilities into the platform.

The product is only the surface.

What would you want to see AgentOS build next? 👀
$AOS

[AgentOS @AgentOS_RH]

The AgentOS token isn't here just to exist.

It's designed to become part of the ecosystem we're building around autonomous agents.

As AgentOS expands, the token will connect the network of agents, developers, infrastructure and the applications built on top of it.

We're building the product first.
The utility follows the network.
$AOS

[AgentOS @AgentOS_RH]

AI agents are becoming the next generation of software.

But building an agent shouldn't mean rebuilding the infrastructure around it.

Memory. Tools. Scheduling. Permissions. Analytics.

AgentOS is the foundation they're built on.

$AOS

[AgentOS @AgentOS_RH]

Today, most AI agents are isolated applications.

Tomorrow, they could be autonomous economic actors.

They'll remember.
They'll use tools.
They'll make decisions.
They'll pay for resources.
They'll work with other agents.

AgentOS is being built for that world.
$AOS