Over the past year, enterprises venturing into AI adoption have grappled with three primary concerns: the fear of selecting an unsuitable model, the worry of inefficient computing power utilization, and, most significantly, the apprehension that Agents, once integrated into systems, might fail to blend seamlessly into human workflows.

After engaging in conversations with more than a dozen enterprises that are experimenting with Agents, we've arrived at a somewhat counterintuitive finding: The real obstacle often lies not in the intelligence quotient of large models but in the fragmented state of an enterprise's office infrastructure.

Documents are scattered across System A, approvals are processed in System B, customer data resides in System C, and meeting minutes are dispersed across various tools. For an Agent to execute a task, it must first pinpoint the locations of data, permissions, and contextual information. More often than not, the issue isn't the Agent's inability to perform the task but its inability to even access the actual work environment.

This brings to light an underestimated challenge: In the era of AI Agents, the role of collaborative office platforms is undergoing a transformation.

Traditionally, enterprises selected platforms based on factors such as communication stability, approval completeness, and the granularity of organizational management. While these factors remain pertinent today, a new critical criterion has emerged: which platform is better positioned to serve as the 'working environment' for Agents.

The focus isn't merely on adding an AI portal or integrating a large model into a chat interface. Instead, it's about whether documents, messages, spreadsheets, tasks, meetings, and processes can be reliably read, invoked, rewritten, and accumulated by Agents.

Recently, Feishu, DingTalk, and WeCom have nearly simultaneously unveiled their official CLIs on GitHub. This move, which may initially appear as a mere update to developer tools, is, in fact, a 'display of underlying capabilities' by these three office platforms for the Agent era.

CLI can be simply understood as a collection of command-line tools. While traditionally serving developers, its importance has surged in the Agent era: Agents excel at invoking structured commands sequentially rather than repeatedly simulating clicks on complex web pages. The platform that more comprehensively exposes capabilities like messaging, documents, spreadsheets, tasks, meetings, and approvals stands a greater chance of becoming the true workplace for Agents.

The disparities quickly become evident.

Let's first examine the most straightforward public data available. Feishu CLI currently boasts 12.2k stars and 813 forks on GitHub; DingTalk DWS has 1.9k stars and 116 forks; and WeCom CLI has 2.1k stars and 149 forks.

For those unfamiliar with GitHub, 'stars' can be likened to 'bookmarks' or 'follows.' Developers star a project if they deem it valuable, making it easier to revisit later. Stars are indicative of popularity and market attention.

Based on this metric, Feishu CLI's attention is approximately six times that of DingTalk and nearly six times that of WeCom.

However, forks provide an even more telling insight.

Forking isn't merely a like; it's when developers 'copy' a project to their own account to study, modify, integrate, or even build upon it for secondary development. In essence, stars represent 'onlookers,' whereas forks signify 'hands-on action.'

From this vantage point, Feishu's lead is even more pronounced: with 813 forks, it's about seven times that of DingTalk and approximately 5.5 times that of WeCom. This suggests that after the three platforms nearly simultaneously opened their CLIs, developers' reactions to Feishu weren't just casual glances but swift progressions to 'taking it for a test drive, modifying it, and integrating it into workflows.'

This is also the signal enterprises should genuinely seek when making selections. CLI isn't just about who exposes more commands but about who makes it easier for developers to construct workflows. In the Agent era, the competition among enterprise office platforms is no longer merely about 'having AI features' but about 'becoming the place where AI truly gets work done.'

Based on currently available public data, Feishu has taken the lead in cultivating stronger developer appeal and clearer hands-on signals.

Historically, enterprises selected collaborative office platforms based on three dimensions: communication efficiency, process management, and organizational connectivity. WeCom excels in connectivity, particularly within the WeChat ecosystem; DingTalk shines in management, especially in approvals, attendance, and organizational control; Feishu excels in integrating documents, messages, meetings, tasks, multi-dimensional tables, and knowledge accumulation into a single workspace.

In the mobile internet era, all three capabilities were valid and catered to diverse enterprise office needs. However, in the AI Agent era, the differences between platforms transcend mere 'app usability' and delve into which platform more closely resembles an organizational system that can be invoked, executed, and accumulated by Agents.

This is why CLI warrants renewed attention from enterprises. It's not just a minor tool within the developer community but a window into whether a platform possesses Agent-ready capabilities. For Agents to penetrate enterprise environments, they must do more than just chat; they must be capable of reading documents, querying data, sending messages, creating tasks, scheduling meetings, writing to spreadsheets, accumulating knowledge, and executing these actions continuously. Whoever can expose these capabilities as stable, clear, and composable interfaces comes closer to being the organizational foundation in the AI era.

From this vantage point, Feishu's advantage becomes more apparent. It's not just about exposing a few capabilities in isolation but about placing high-frequency collaboration scenarios within an integrated workspace and then exposing it to developers and Agents via CLI. For enterprises, this means Agents don't have to repeatedly jump between multiple systems or reassemble fragmented contexts but can complete a closed loop from information acquisition to task execution and result accumulation within a single platform.

This is also one of the reasons Feishu has garnered higher stars and forks on GitHub. Developers pay attention to it not just because it exposed a tool but because it represents a more complete workflow environment. Stars indicate it's being noticed; forks indicate it's being tested, modified, and integrated into real-world scenarios. For enterprises, these 'hands-on signals' from developers hold more value than mere feature announcements.

Therefore, redefining DingTalk, Feishu, and WeCom in the AI era necessitates moving beyond traditional office software classifications. WeCom is not just a communication tool; DingTalk is not just a management tool; Feishu is not just a collaboration tool. They are vying for a new position: who can become the gateway and foundation for Agents to enter, understand, and execute organizational tasks.

Based on current public openness, developer feedback, and workflow extensibility, Feishu appears closer to this answer.

When enterprises select an office platform today, they are essentially choosing the future workplace for AI Agents. In the past, platforms derived value from where people worked; in the future, platforms will become the infrastructure where Agents can work continuously. By this new standard, Feishu is not just a more modern collaboration tool but the organizational foundation in the AI era that enterprises should prioritize.