Farewell to Standalone Chat Interfaces, Hello to the Collaborative Infrastructure Era
Over the past six months, AI-powered office tools have flooded the market en masse. After observing enough of them, you’ll notice a widespread affliction: “chat interface dependency”—as if wrapping a large language model in a UI shell and adding a sidebar is enough to handle all complex business workflows within an enterprise.
But standalone chat interfaces will never reach the deep waters of real-world business operations.
You ask a third-party AI assistant in a chat window to draft a flawless project post-mortem report—and then what? You must manually copy the text, switch windows, paste it into a document, and individually @ each relevant stakeholder. This fragmentation demotes “intelligence” down to little more than an advanced text generator.
Building nothing more than a chat interface shell inevitably confines you to the periphery of business operations.
When intelligent work agents begin tackling long-horizon tasks, what kind of execution environment do they truly require? The answer is unequivocal: a genuine collaborative infrastructure. If we consider general-purpose agents as super-apps of the AI era, then the collaborative infrastructure is their operating system. Without such infrastructure to serve as a foundation, an agent is at best a brain without hands or feet.
This is precisely why platforms like Lark—enterprise-grade collaborative infrastructures—are naturally fertile ground for AI adoption. They need not compete on flashy, point-specific features; instead, their foundational capabilities—real-time messaging, multi-dimensional spreadsheets, documents, calendars, video conferencing, and approval workflows—are already sufficient to provide AI with the most complete contextual awareness and workflow integration channels.
Once a third-party AI assistant integrates into the Lark ecosystem, it ceases to be an isolated chat interface—and instead becomes genuinely embedded within the enterprise’s operational flow.
For general-purpose agents to become operational, deep integration between models and collaborative infrastructure is a prerequisite.
So how, exactly, does this infrastructure support complex, long-horizon task orchestration?
Long-Horizon Task Orchestration: Enabling Intelligent Work Agents to Integrate into Real Workflows
Many envision that simply connecting intelligent work agents to business systems will enable fully automated execution of lengthy industry research reports—or cross-departmental data reconciliation involving tens of thousands of words.
In reality, the greatest risk in long-horizon tasks isn’t insufficient model intelligence—it’s mid-process “blackouts” or derailment.
A common misconception persists in the industry: that a sufficiently large context window allows AI to handle everything in one go. That simply doesn’t hold up in real enterprise settings. Complex long-horizon tasks must be decomposed—and they rely heavily on human intervention and course correction at critical junctures.
Once a third-party AI assistant integrates into the Lark ecosystem, the logic behind long-horizon task orchestration instantly becomes clear.
The agent breaks down major tasks into parallel subtasks running in the background—eliminating the need for users to stare at their screens awaiting results. Lark’s real-time messaging serves naturally as the lightest-weight, most responsive “reporting channel.”
Upon completing a key phase, the agent delivers a concise node summary directly in the chat. With just a glance, you confirm the direction remains sound—then effortlessly create a follow-up task to nudge downstream stakeholders, or initiate an approval workflow to advance the process.
No cumbersome system switching. No jarring pop-up interruptions.
Full automation is a myth; controllable human–AI collaboration is where true implementation happens.
Lark’s foundational capabilities—messaging and approvals—reliably anchor the “human confirmation” step within agent orchestration, ensuring AI outputs smoothly translate into concrete business actions.
Yet this raises another thorny question: Where should the substantial volume of intermediate data and fragmented information generated while agents process subtasks be stored?
Context Management: Building Enterprise-Grade Memory with Multi-Dimensional Spreadsheets and Documents
Where should intermediate data reside? This is a fundamental bottleneck every long-horizon task must confront.
Many treat context as an infinite black hole—dumping content into it indiscriminately. In truth, context is a finite computational resource requiring active management.
Once a model’s “short-term working memory” overloads, it either hallucinates or forgets critical instructions. Relentlessly extending prompts to sustain memory not only inflates costs but also frequently yields dismal results.
The smarter approach is to “outsource” memory.
Lark’s multi-dimensional spreadsheets and documents are perfectly suited to serve as the “external brain” for third-party AI assistants. When an intelligent work agent completes a cycle within a long-horizon task—extracting volumes of unstructured customer feedback or business updates—it needn’t vomit a chaotic wall of text into the chat. Instead, it systematically archives those fragments into a multi-dimensional spreadsheet.
As for lengthy analytical reports spanning tens of thousands of characters, attaching them directly to a Lark document is clearly the more elegant solution.
This archiving is no closed, siloed exercise. Thanks to Lark’s open ecosystem, diverse third-party AI office tools can seamlessly connect to these structured components. Agents write core metrics into spreadsheets and preserve reasoning logic in documents—transforming ephemeral conversational streams into enduring, enterprise-grade digital assets.
Reserve compute power for reasoning; entrust memory to the infrastructure.
Yet this represents only static knowledge archiving. The real test begins when data in spreadsheets and conclusions in documents must be reactivated—and even used to trigger the next business action.
Cross-Component Collaboration: Closing the Loop from Content Generation to Business Execution
Static archiving is merely the first step. Only when data truly “starts moving” do we enter the deep waters of collaborative infrastructure.
Consider a typical project management scenario: A team uses a third-party intelligent work agent to generate a comprehensive draft of its quarterly project plan. If left confined to a chat interface, this remains nothing more than unactionable text.
Within the Lark ecosystem, however, the workflow looks entirely different.
The project lead reviews the draft in a Lark document and highlights key milestones with a single click. Then—without switching windows—they convert those highlighted points directly into scheduled events in the Lark calendar.
AI handles ideation and generation; humans handle convergence and orchestration.
That’s the authentic office experience—not mystical “fully automated workflows,” but humans leveraging AI suggestions via the Lark calendar to turn plans into time-bound, reminder-equipped schedules—and assigning accountability to specific individuals.
Now consider a meeting scenario. After a two-hour video conference concludes, a third-party AI assistant rapidly distills key takeaways for post-meeting review.
What the operator does next is far from mechanical copy-pasting. Instead, they receive these structured recommendations in a Lark document, make minor adjustments, and immediately link them to the transcript of that very video conference. Then, they manually select core action items and push them precisely to the relevant executors’ chat windows using Lark’s real-time messaging.
From documents to calendars, from video meetings to instant messages—the boundaries between components dissolve naturally through human action.
This cross-component collaboration essentially constructs a “model-native” runtime environment. AI office tools supply cognitive horsepower; Lark supplies hands, feet, and nervous system.
People stay rooted in the infrastructure—and business flows naturally.
When content generation ceases to be an isolated endpoint—and instead becomes the starting point for the next business action—we may need to reconsider: What truly constitutes the productivity moat in the AI era?
Final Outlook: Open Ecosystems Determine the Depth of AI Adoption
When we shift our focus away from parameter-count competitions among large models and return to real-world workplaces, the answer becomes strikingly clear.
The ultimate destination of AI-powered offices has never been a singular, omniscient super-application.
Rather, it lies in whoever provides a sufficiently open, standardized collaborative infrastructure—enabling seamless integration of diverse third-party AI assistants and intelligent work agents. What Lark is building is precisely this foundational “road-building” work—leveraging open APIs for its foundational components (real-time messaging, documents, multi-dimensional spreadsheets) to translate model-level computational power into tangible enterprise business flows.
Of course, no matter how robust the infrastructure, it remains useless unless people walk on it.
I’ve observed a telling phenomenon: Most users still treat AI office tools as nothing more than advanced chat interfaces—and default to manually decomposing complex tasks and executing them point-by-point.
The habit of entrusting full, intricate long-horizon tasks to AI-assisted collaboration has only just begun to emerge.
This isn’t merely a technical barrier—it’s a shift in organizational mindset. As increasing numbers of intelligent work agents flood into the Lark ecosystem—conducting compliance checks during approvals, coordinating scheduling in calendars, and archiving knowledge in multi-dimensional spreadsheets—the true deep-water zone will finally arrive.
Ecosystem vitality and scenario depth are the enduring barriers that cannot be easily erased.
And the co-evolution of general-purpose agents with this ecosystem has only just begun.