💡 TL;DR: Most AI agents are trapped in limited and one-size-fits-all sandboxes, while MuleRun is the first global platform to solve this by providing a fully customizable and persistent agent runtime, allowing you to define the OS, access native software, maintain state across sessions, and allocate hardware resources. This empowers you to build true “Digital Workers,” not just limited chatbots.
Imagine you’re a brilliant human consciousness trapped in a vat, with electrodes feeding you sensory experiences of a world you can never truly touch. You can think, reason, and plan—but your actions are forever constrained by the artificial boundaries of your container.
This isn’t just a philosophical thought experiment. It’s the current reality for most AI agents today.
The Runtime Blindspot
The AI community has an obsession problem. We endlessly debate model architectures, fine-tune prompts, engineer memory systems, and build sophisticated tool integrations. Yet we systematically ignore one of the most critical components: where our agents actually execute.
Most agents today are essentially “brains in vats”—powerful reasoning systems trapped in severely limited execution environments. Consider the current landscape:
Browser-only agents
like ChatGPT’s Operator can only interact through web interfaces, clicking buttons and filling forms within browser constraints
Containerized solutions
like Manus use e2b virtual machines with browser + code sandbox, but no vision capabilities or GUI software installation capabilities
The fundamental limitation? None of these platforms allow agents to configure their own runtime environment. This is like hiring a software engineer but only giving them access to a browser with a calculator extension.
Why Runtime Is Everything
Local Filesystem as External Memory
The difference between browser-based and computer-based agents becomes stark when we examine memory and persistence. Browser automation agents are limited to web interactions and cannot access local storage or maintain persistent state across sessions.
Real agents need persistent memory beyond context windows. Claude Code writes TODO files as external working memory. Manus stores longer contexts in local files. These aren’t workarounds—they’re fundamental patterns for building agents that maintain state across complex, multi-session workflows.
With a proper runtime, agents can:
- Maintain project workspaces that persist across interactions
- Store and retrieve contextual information beyond token limits
- Build cumulative knowledge bases that improve over time
- Cache expensive computations and intermediate results
Self-Modifying Tool Creation
Agents with full runtime access can literally write their own tools—it’s where agents become autonomous. They can:
- Write custom scripts for specific workflow automation
- Build domain-specific utilities as needs arise
- Create integration bridges between different software systems
This creates a compounding effect where agents become more capable over time by expanding their own toolkit.
Local Libraries and Software Selection
The files, libraries, tools, and software you choose will define a large portion of an agent’s ability. When agents have access to pre-installed software and libraries selected by creators, they gain native capabilities that no other approach can match.
For example, imagine trying to code a video downloader from scratch in every session. You’d be trapped in an endless cycle of trial-and-error AI coding, wasting time and tokens. MuleRun’s All-In-One Downloader frees you from this by leveraging powerful, pre-installed open-source tools.
The MuleRun Difference
At MuleRun, we recognized this runtime gap from day one. Our Creator Studio allows agent creators to define and customize complete runtime environments for their agents:
- Define OS, installed software, and hardware specifications
- Set network policies and security boundaries
- User can save runtime configurations for future sessions
- The same runtime can be reused with different agents
The table below compares MuleRun Runtime with other agent execution approaches, highlighting not only what sets us apart, but also how we deliver a more powerful and flexible environment for your agents.
| Capability | No Visible Runtime (Zapier, Make.com) | Limited Runtime (Manus, ChatGPT Operator) | Full Configurable Runtime (MuleRun) |
|---|---|---|---|
| Configurable Environment | ❌ | ❌ | ✅ |
| Native Software Access | ❌ | ⭕ | ✅ |
| Persistent Filesystem | ❌ | ❌ | ✅ |
| Hardware Provisioning | ❌ | ❌ | ✅ |
| Cross-Session State | ❌ | ✅ | ✅ |
❌ Not Supported, ⭕️ Limited Supported, ✅ Supported
MuleRun Examples On Agent With Runtime
The Honkai: Star Rail Booster automates complex tasks directly within the game client. This is achieved by running the agent in a Windows environment with pre-installed software and dedicated GPU access, demonstrating a powerful form of environmental control that browser-based approaches cannot provide.
Automating a game like this demands several core runtime capabilities. Here’s a side-by-side comparison of how MuleRun stacks up.
| Capability | Why It's Essential for Game Automation | No Visible Runtime (Zapier, Make.com) | Limited Runtime (Manus, ChatGPT Operator) | Full Configurable Runtime (MuleRun) |
|---|---|---|---|---|
| Native Software Access | Honkai: Star Rail is a complex, multi-gigabyte Windows application. The agent must be able to launch and interact with the game's client directly. | ❌ | ⭕ The game cannot be run locally. It can be opened through a browser, while it fails to launch due to latency and hardware limitations. | ✅ |
| Dedicated GPU Access | Modern 3D games are graphically intensive and require significant GPU power to render worlds and characters. Without it, the game won't even launch. | ❌ | ❌ | ✅ |
| OS-Level I/O Control | Game automation demands precise, unrestricted control over mouse movements, clicks, and keyboard inputs at the operating system level to navigate menus and control characters. | ❌ Keyboard or mouse I/O is not supported | ❌ Keyboard or mouse I/O is not supported | ✅ Keyboard or mouse I/O is supported |
❌ Not Supported, ⭕️ Limited Supported, ✅ Supported
From Vat to Workshop
The philosophical puzzle of the “brain in a vat” asks: how would you know if your reality was constrained by artificial boundaries?
For AI agents, the answer is simple: give them a real computer.
As Anthropic notes in their research, “A vast amount of modern work happens via computers. Enabling AIs to interact directly with computer software in the same way people do will unlock a huge range of applications that simply aren’t possible for the current generation of AI assistants.”
The future of AI agents requires better prompts, bigger context windows, sophisticated reasoning, and the runtime environments they need to become true digital workers. While the community has made tremendous progress on reasoning capabilities, we must also break agents out of their artificial constraints to unlock their full potential.
Start Building with Real Computers Today
Ready to create agents in a fully configurable runtime? We’re building this in the open and would love for you to join us on the journey. Here are the best ways to get involved:
- 🚀 Become a Genesis Creator: Apply here to get early access and exclusive benefits.
- 💬 Chat with the Team: Join our Discord to hang out, ask questions, and share ideas.
- 🐦 Stay in the Loop: Follow us on X for tech deep-dives and early API access.