Office AI Agents Tested: Ecosystem Collaboration vs. Local Execution Routing Practices

1. The “Homogenization Illusion” in the AI Agent Boom

Over the past year, AI agents for office scenarios have launched with unprecedented density. From initial coding-focused developer series to today’s office suites covering presentation creation, website building, content generation, skill orchestration, and system cleaning, the iteration speed is almost weekly. Many developers’ first impression is that, looking solely at feature lists, these agents seem largely identical with highly overlapping capabilities.

However, running a complete real-world task reveals that homogenization is merely superficial. Actual performance is never determined by the length of the feature list, but by the underlying architectural route. Current mainstream office agents generally fall into two categories:

• Ecosystem Collaboration Agents: Built-in proprietary Large Language Model (LLM) matrices (typically offering a high-throughput lightweight tier and a more capable flagship tier). The core strategy is deep integration with mainstream IM and collaborative work platforms. Through platform connectors, they provide one-stop operations on cloud assets like messages, cloud documents, and approval flows, focusing on multimodal task processing.
• Local Execution Agents: Focus on local system operations—file management, cross-application automation, and browser control—using LLMs as the core for information synthesis.

One concentrates engineering resources on a “cloud ecosystem closed loop,” while the other bets on “local execution depth.” This choice cascades down to the performance of almost every task category.

2. Architectural Essence: Authorization Chains and Execution Environments

Ecosystem Collaboration: Connector-First Low-Friction Design.
A typical characteristic of these agents is an extremely short authorization chain. When connecting to an IM collaboration platform, it often requires only a one-click authorization for the account. Without individually checking approval items, it can operate cloud assets like chat content and cloud documents on that platform. Once connected, a conversational agent entry point appears directly within the platform. Technically, this relies on pre-configured authorization templates and API encapsulation: user-side permission configuration is compressed into a single click, and the invocation chain runs entirely over stable cloud APIs. Thus, it is fast and has a high success rate for structured tasks.

However, it has an easily overlooked shortcoming: the platform-side entry and the PC client are usually not interoperable. Their task conversations are independent. The platform-side entry can only handle cloud tasks and cannot reversely control the local computer. For users with both cloud and local needs, this “semi-closed loop” is a definite experience loss.

Local Execution: Slow and Steady via Desktop Depth.
This route bets on the other end: directly operating file systems, system configurations, and browsers via desktop automation frameworks, combined with LLMs for understanding and summarization. Connecting to external platforms is usually more cumbersome—requiring step-by-step permission selection and administrator approval, resulting in a long chain and high upfront time cost. In return, it gains direct control over local files, system configurations, and arbitrary websites. The divergence in the following five tasks stems right here.

3. Evaluation Methodology: Five Tasks for Five Capability Dimensions

Fragmented testing of “whatever is trending” yields conclusions with no transfer value. A scientific approach is to design tasks by capability dimension. The five tasks selected in this article correspond to five core capabilities:

1. IM message to knowledge file → Platform connector invocation and information extraction;
2. Multi-dimensional table and dashboard generation → Structured data understanding and generation;
3. Local file batch processing → Local execution and environment perception;
4. Website operation → Browser automation and dynamic page understanding;
5. Web OS-level application development → Long-chain code generation and self-correction.

Evaluation metrics should be fixed to four items: Task completion rate, end-to-end latency, error-correction recovery capability, and direct usability of results. Let’s break them down one by one.

4. Task 1: IM Message to Knowledge File — Authorization Chain Determines First Impression

The task is straightforward: extract chat logs from a specified conversation, denoise, and summarize them into a structured knowledge document. The technical chain is: Platform Auth → Message Fetching → Info Extraction & Cleaning → Doc Generation → Cloud Doc Write-back.

In testing, the two routes differed vastly. The Ecosystem Collaboration agent, benefiting from one-click authorization and pre-set API encapsulation, smoothly went from authorization to document output in minutes. The extraction quality of speakers, timelines, and to-do items was stable. The Local Execution agent, constrained by the long chain of “checking permissions individually + admin approval,” consumed significant time upfront. Subsequent stages were prone to incomplete paginated message fetching and timeline errors, making the overall process noticeably slower.

Conclusion: For tasks where the main battlefield is cloud assets on collaboration platforms, the Ecosystem Collaboration agent is nearly crushing. But the constraint of “platform-side and client-side tasks not interoperating” must be clearly understood before selection.

5. Task 2: Multi-dimensional Table and Dashboard Generation — The Watershed of Structured Capabilities

Generating multi-dimensional tables and dashboard views is a classic scenario for testing structured understanding: extracting fields from raw materials, designing table structures, populating data, and generating dashboards grouped by dimensions.

In testing, the Ecosystem Collaboration agent directly wrote to tables via native platform APIs. Field types, single/multiple options, and grouping configurations were essentially spot-on in one pass, and the output was directly deliverable. The Local Execution agent frequently encountered issues—date fields misjudged as text, grouping dimension configurations scrambled, and dashboard cards failing to associate with data records—often requiring multiple rounds of manual rework.

The underlying reason is that structured generation relies on precise mapping to the target platform’s data model. Direct API writing inherently holds the advantage. The approach of “general generation then forcing into platform structure” amplifies errors progressively across three stages: field mapping, type conversion, and view configuration.

6. Task 3: Local File Batch Processing — A Mirror for Engineering Rigor

In local scenarios, the tables turn. A typical task: traverse a specified directory, rename by rules, convert formats, and batch-generate summary documents. Both types of agents could complete this with quality, so the evaluation focus shifted to engineering details:

• Does it provide a dry-run preview first and execute only after confirmation to avoid irreversible mistakes?
• For locked, unauthorized, or corrupted files, does it have a skip and alert mechanism?
• Are batch operations idempotent, and can they resume from breakpoints after interruption?
• Does it output complete execution logs for post-auditing?

These details don’t change the conclusion of “whether it can be done,” but they directly determine “whether you dare to use it in a production environment.”

7. Task 4: Website Operation — Gradual Improvement in Dynamic Environments

Website operation is the touchstone for browser automation: form filling, data scraping, multi-step process execution. It requires the agent to understand DOM structures, handle dynamic loading, and maintain session states.

The Local Execution agent showed a trend of “gradual improvement”: initially experiencing element location failures and missed operations due to insufficient wait strategies. However, as its cognitive accumulation of pages grew (many implementations cache page structure understanding), the success rate and fluency climbed significantly. These solutions typically employ a dual-channel location strategy of “visual understanding + DOM parsing,” adapting better to long-tail websites without open APIs. The Ecosystem Collaboration agent’s web operation relies more on the target platform’s open capabilities. Once outside its own ecosystem, its generalization capability is immediately limited.

8. Task 5: Web OS-Level Development — The Ceiling Test for Code Generation

Let’s set an extreme stress test for code generation: implement a Web OS from scratch—a complete web operating system including a desktop, window management, taskbar, virtual file system, and multi-app switching. It tests not single-point code quality, but three things:

• Long-chain planning: Can it break down the macro goal into rendering layers, event systems, state management, local storage, etc., and maintain interface consistency between modules?
• Long-context self-consistency: As code volume grows, are naming conflicts, state pollution, and circular dependencies controllable?
• Self-correction: Can it autonomously locate and fix runtime errors instead of throwing them back to the user?

In testing, the Local Execution agent demonstrated a clear advantage: rational module splitting and complete interaction details, marking its highlight moment. The Ecosystem Collaboration agent, limited by its optimization focus on office tasks, performed averagely in deep development. This validates a rule—the capability ceiling of an agent is determined by its architectural focus. A route investing more resources in system-level operations and code generation naturally goes deeper in development tasks.

9. Selection Advice: Route by Scenario, Don’t Seek an “All-Around Player”

Synthesizing the five tasks, the selection logic converges into a checklist:

• Heavy cloud collaboration users (main battlefield is IM platforms, cloud docs, approval flows): Ecosystem Collaboration is the first choice. Low auth friction, stable structured tasks, high speed.
• Developers and local automation needs (heavy local files, system configs, long-tail websites without APIs, prototype dev): Local Execution is the first choice. Deeper depth, higher code generation ceiling, but accept the speed trade-off.
• Enterprise deployment: No need to choose one. Route tasks to a combined usage via a unified task routing layer entry point.

The schematic logic for task routing is as follows:

def route(task):
    # Cloud structured tasks: prioritize low-friction, high-stability ecosystem collaboration route
    if task.target in [IM_CHAT, CLOUD_DOC, APPROVAL_FLOW]:
        return ECOSYSTEM_AGENT
    # Local execution and deep development: assign to local execution route
    if task.target in [LOCAL_FILES, SYSTEM_CONFIG] or task.deep_code_gen:
        return LOCAL_AGENT
    return ECOSYSTEM_AGENT   # Default to low-friction route

For enterprises wishing to build their own office agents, they can also leverage general capabilities provided by cloud vendors (such as Baidu AI Cloud’s LLM services, knowledge retrieval, and workflow orchestration capabilities). By combining these with their own IM and office systems, they can build dedicated agents, designing connector authorization, task isolation, and permission approvals once within their own controllable system.

When solidifying evaluation conclusions, it is recommended to cover at least seven dimensions: authorization and permission chain length, structured generation quality, local execution engineering rigor, browser automation success rate, code generation ceiling, and two often-overlooked but critical capabilities—whether cloud and local tasks are interoperable, and whether failed tasks are recoverable.

10. Conclusion: Every Architecture Has Its Trade-offs

Testing across five tasks shows that no single route can dominate all scenarios. Ecosystem Collaboration agents reign supreme in cloud structured tasks but falter in deep development and long-tail website operations. Local Execution agents are slower but excel in local execution and code development. This is not a difference in product quality, but an architectural inevitability—capability boundaries grow where engineering resources are invested.

For individual users, the rational approach is to abandon the obsession with finding the “strongest agent” and build a habit of “routing by task type.” For enterprises, rather than asking “which agent is best,” it is better to map out your high-frequency task lists and data boundaries first, letting the architectural selection emerge naturally. Agent iterations will only accelerate, and a task-centric evaluation method remains the reusable anchor through rounds of product updates.