AgentOS @AgentOS_RH

The value of an agent isn't just what it knows, it's what it can actually do. We've been continuing work on how AgentOS agents connect with external tools and capabilities. The bigger vision is an expanding ecosystem where developers don't have to rebuild every capability from scratch. Find the tool -> Connect it to the agent -> Define the permissions -> Let the agent use it. More on this soon. 👀 $AOS

Another part of AgentOS we've been improving: How agents are managed after they're created. Creating an agent is one thing but operating it over time is another. We're continuing to refine the lifecycle around agents from deployment and updates to pausing, resuming, cloning and managing different versions. Agents shouldn't be static instead they should be infrastructure you can continuously evolve $AOS

Agents are becoming more capable. Which makes control more important, not less. We've been refining the control layer around AgentOS agents continuing to improve how developers define what an agent can access, what it can do and where it needs approval. Autonomy doesn't mean unlimited permissions. The best agents should know exactly where their boundaries are. More improvements to the control layer coming. $AOS

Small SDK improvement shipped We're continuing to make it easier for developers to build on top of AgentOS and bring agents into their own applications. The end goal isn't to create another complicated framework developers have to learn. It should feel simple: -> Create an agent. -> Configure it. -> Connect it. -> Run it. The infrastructure stays in the background. That's what we're optimizing for. $AOS

MEMORY EVOLUTION We've pushed another iteration to the AgentOS memory layer. An agent's memory shouldn't just be a pile of old conversations. We're continuing to improve how information can be structured, scoped and managed across agents, users and sessions. Because the more autonomous an agent becomes, the more important it is to control what it remembers and when. A lot more is being built into the memory layer an this is only one piece of it. $AOS

Another improvement pushed to AgentOS. ⚡ We're continuing to refine the way agents are configured and managed from the dashboard. Models, instructions, tools, permissions and agent behavior should be easy to control without turning every change into an engineering task. The idea behind AgentOS has always been simple: Build the intelligence. Let the infrastructure handle the rest. More improvements rolling out behind the scenes. 👀 $AOS

Small update, but an important one. We've continued refining how AgentOS agents handle execution behind the scenes. The goal is to make agents more reliable as they move between reasoning, tools and actions with less friction between each step. A lot of the infrastructure work won't always be visible but that's kind of the point. The better the foundation gets, the less developers should have to think about it. Still shipping. 👀 $AOS

There’s a lot happening behind the scenes at AgentOS right now. We’ve been actively working on improving the infrastructure that already exists, expanding what agents can do, and building new capabilities into the AgentOS ecosystem. The vision has always been bigger than simply creating an AI agent. We’re building the infrastructure around autonomous agents memory, tools, permissions, scheduling, APIs, on-chain capabilities and the layers that allow agents to actually operate. And we’re not slowing down. A number of updates, integrations and partnerships are already in the works, with a lot planned for the coming weeks. The team has been heads down building. We’ll let the updates do the talking. 👀 Back to work. 🫡 $AOS

We've just pushed another improvement to the Automated Liquidity Management layer inside AgentOS. Continuing to refine how liquidity agents are configured, deployed and operated making the experience more seamless as we build out the AiFi stack. The first version was just the beginning. More iterations & more new updates coming soon. 👀 $AOS

AgentOS @AgentOS_RH

3 weeks ago

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

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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 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. 👀

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

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

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 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

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

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

🔐 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

$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. 👀

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: app.agentos.llc