AI-Native Work: Shifting from Operator to Decision-Maker
Traditional office software like the Office suite (Word, Excel, PowerPoint) requires users to be deeply involved at the operational level, memorizing shortcuts, adjusting formats, and studying complex formulas. However, according to a user experience report from May 29, 2026, the emerging AI-native work model is changing this. Take the AI agent platform MuleRun, for example: it shifts the user’s role from an ‘operator’ to a ‘decision-maker’.
In MuleRun, users can complete complex tasks simply by giving commands in natural language. For instance, a user can instruct an AI agent via WeChat: ‘Create a Q2 business report PPT based on the document in Lark.’ The AI agent can autonomously connect to data sources, understand the content, generate a first draft, and make revisions based on follow-up questions, such as ‘Change the content on page three to a bar chart for business comparison.’ The entire process doesn’t require opening traditional software like PowerPoint, enabling seamless cross-device and cross-platform editing and sharing. The user’s focus shifts from ‘how to present’ to ‘what to express.’
MuleRun Computer: A Persistent Cloud-Based Automation Engine
The MuleRun platform offers a core feature called ‘MuleRun Computer.’ This can be understood as an independent, 24/7 cloud computing environment dedicated to executing various automated tasks (Skills) without consuming local device resources.
In enterprise scenarios, users can deploy recurring tasks like periodically checking business data or pulling reports to the MuleRun Computer for unattended automation. Additionally, the platform integrates some proprietary tools to enhance its ecosystem:
- Data Source: Features built-in datasets for specific domains, such as e-commerce data. Users can directly call on this data for analysis, for example, by querying ’global trends in top-selling product categories during Mother’s Day,' to support business decisions.
- Page: Provides a one-click publishing feature, allowing users to quickly deploy and share AI-generated web content, eliminating the complex steps of traditional server deployment.
Multi Agent Task: An Innovative Solution to ‘Context Pollution’
MuleRun’s recently updated ‘Multi Agent Task’ feature is a significant exploration in the field of multi-agent collaboration. A Multi-Agent System is a mainstream architecture in AI for solving complex problems. Its primary goal is to avoid ‘context pollution’—the information confusion and decreased reliability that occur when a single, all-powerful agent becomes overloaded by handling multi-stage, multi-type information in its context window.
There are two common solutions in the industry:
- Parallel Model: The main agent breaks down a task into multiple independent sub-modules, which are executed simultaneously by different sub-agents. The results are then aggregated.
- Pipeline Model: A long task chain is broken down into a sequence of connected steps, processed sequentially by specialized agents that can also cross-check each other’s work.
From ‘One-Off Tools’ to ‘Persistent Team Members’
MuleRun’s unique approach to multi-agent design lies in the ‘persistence’ of its sub-agents. In most products, sub-agents are destroyed upon task completion, making them essentially one-off execution tools. MuleRun, however, preserves the agents created for specific sub-tasks.
Take the complex task of ‘creating an integrated marketing plan’ as an example. The main task is broken down into several sub-tasks: PPT creation, content for the X platform, a YouTube video script, and Instagram asset creation. Users can not only view the independent window and progress of each sub-task in real-time but can also continue to interact with any of the sub-agents after the task is completed.
For instance, if a user is not satisfied with the content plan for the X platform, they can directly enter that sub-agent’s independent chat interface to suggest changes. This sub-agent retains its dedicated work history and context, allowing it to iterate and optimize based on previous conversations without restarting the entire main task. This design transforms AI agents from tools that disappear after a task is done into ‘team members’ with their own context, capable of long-term collaboration, making the human-computer interaction experience much closer to a real team’s workflow.
