On September 2, Tencent officially open-sourced its AI agent framework Youtu-Agent.

According to official information from Tencent, the framework achieved a 71.47% accuracy rate based on DeepSeek-V3.1 on the WebWalkerQA benchmark, setting a new record for open-source models.

Alibaba, Tencent, and Microsoft are all developing open-source AI agent frameworks, with Microsoft having done similar work two years ago. So what exactly are their objectives?

We notice that different companies use different keywords in their narratives: Alibaba emphasizes "developer-friendly," Tencent emphasizes "application implementation," while Microsoft directly embeds agents into its Office and Copilot ecosystem.

These seemingly scattered moves actually point to the same unproven future—AI agents may become the new digital entry point of the AI era.

The issue is that the value of this entry point has not yet been validated at scale in real business operations.

Therefore, the giants choose to test the waters in the open-source community first, handing the stage over to developers and early users, while also securing their positions: if the future indeed unfolds as currently anticipated, they won't lose their first-mover advantage.

Thus, this AI agent framework competition is destined to be far more complex than mere technical rivalry.

**Conservative Tencent and Aggressive Alibaba**

The giants' sudden collective display of such "selflessness" in opening AI agent frameworks appears magnanimous. However, in the industry, open-sourcing is more commonly interpreted as a low-cost market entry strategy.

The biggest problem with AI agents currently is "whether there are real scenarios that can actually work": from file management and paper retrieval to data analysis, existing frameworks provide numerous functions, but these functions are scattered across fragments of most enterprises' daily work and have not yet proven capable of significantly reducing costs or increasing revenue.

In other words, AI agents have enormous imaginative potential but lack large-scale empirical evidence. No enterprise is willing to make major investments in unvalidated technology.

If giants rashly push frameworks toward commercial use, they would bear extremely high uncertainty and opportunity costs.

Therefore, open-sourcing becomes the safest approach: let developers experiment, let communities incubate possible scenarios, and whoever's framework gets used by more people will have the initiative in future standard competition.

This is not pure "openness" but more like insurance: showcasing technological presence while transferring risk and invisibly defining spheres of influence.

When the market matures and scenarios work someday, they can tighten ecosystem barriers—such cases are not uncommon in history.

Tencent's newly launched Youtu-Agent is described as an open-source framework that can support tasks such as local file management, data analysis, and paper research.

Official statements claim that based on the WebWalkerQA benchmark, this framework set new test records—but there's no need to celebrate, as this is just a routine move to demonstrate technical strength.

Actually, from a product positioning perspective, Tencent's intention in launching this framework is not to "define new entry points" but rather appears to be a cautious exploration.

Tencent's long-term strategic preference has been to "cultivate deeply within existing ecosystems," such as social networking, gaming, and enterprise services. Compared to Alibaba's active exploration of AI organizational forms, Tencent's attitude toward AI agent frameworks appears more cautious.

For instance, this time Tencent provides very practical functions—files, data, papers—all scenarios that researchers and developers would use. This choice avoids "over-promising" but also shows that Tencent values the dimension of "first implementing in real applications, then discussing ecosystem scale."

This resembles risk hedging. Because if AI agents cannot be embedded in daily workflows at scale, they can hardly become true platform entry points.

Therefore, rather than loudly proclaiming the future, it's better to find a trace of real demand in local applications.

Compared to Tencent's caution, Alibaba's AgentScope 1.0 appears much more aggressive: emphasizing full lifecycle management of "development, deployment, and monitoring," attempting to build a one-stop multi-agent development platform.

Such design goals reflect Alibaba's consistent strategy—entering from the platform level, building basic infrastructure, then expanding a complete ecosystem around it.

In Alibaba's narrative, AI agents are not just tools but could also become catalysts for new organizational forms (such as AI organizations). Alibaba hopes that through frameworks, developers won't need to worry about operation and monitoring issues when building complex applications, leaving complexity to the platform to solve.

Compared to Tencent's careful exploration within its own ecosystem, Alibaba's choice represents a larger-scale bet.

In the past, Alibaba's multi-dimensional exploration through cloud computing, collaborative office solutions, and retail technology has given it experience in promoting platform-based products.

AgentScope 1.0 is therefore packaged as a "universal framework," covering as many potential scenarios as possible. But this broadly spread strategy also means Alibaba must wait for more scenarios to emerge naturally, otherwise the platform's value will be difficult to realize.

**Microsoft: On Another Path?**

Compared to domestic giants, Microsoft's approach appears bolder and more confident, or perhaps more pragmatic and direct. This is mainly because Microsoft launched similar frameworks two years ago.

Therefore, Microsoft now chooses to directly embed AI agent capabilities into Office suites and Copilot.

The logic of this approach is clear: users already use Word, Excel, and Outlook daily, so embedding agents as enhanced features makes scenarios naturally exist.

Microsoft is not rushing to compete for framework standards in the developer community but leverages its existing user base to turn agents into ready-made productivity tools.

For Microsoft's vast user base—enterprise users—this path actually has higher acceptance: enterprises don't need to learn an additional new framework or worry about deployment and monitoring, they just need to continue using familiar software to gradually experience the value of AI agents.

This is an "application-driven ecosystem" approach. In contrast, Alibaba and Tencent prefer to first encircle developers (both individuals and enterprises) through frameworks, then expect natural growth of application scenarios.

Microsoft's choice essentially skips this uncertain phase. However, the risks it faces are actually greater than its domestic counterparts, but since Microsoft only provides an auxiliary AI tool, enterprises can choose to use it or not, so sunk costs need not be overly considered.

We note that behind this lies Microsoft's resource endowment: hundreds of millions of users' productivity software usage habits and Azure cloud as underlying support.

Microsoft's framework advantage lies not in open-sourcing to attract developers but in the "strong binding" of its ecosystem—users have almost no choice to escape.

But this doesn't mean Microsoft has no interest in open-source AI agent frameworks. As early as September 2023, Microsoft launched a similar AI agent framework—AutoGen: for building and managing multi-agent systems.

AutoGen's core idea is to have multiple agents (which can be large language models, tool calling modules, human participants, etc.) collaborate through conversational interaction to complete complex tasks.

These agents can have different roles and capabilities, such as some focusing on logical reasoning, some excelling at calling external tools (like code execution, database queries), and some responsible for coordinating task workflows.

**Why Do These Giants All Choose to Build AI Agent Frameworks?**

On the surface, each company's reasons for launching frameworks differ: demonstrating technical strength, serving developers, promoting scenario implementation. But the deeper reason actually points to one question—where is the entry point?

If the future application world consists of countless AI agents, then whoever's framework becomes the de facto standard will have the opportunity to define interaction rules and allocate traffic entry points.

This is no different from the competitive logic of operating systems, browsers, and mobile app stores in the past.

Precisely because of this, even though AI agents have not yet been embedded in enterprise operations at scale, each company must plan ahead.

Open-sourcing is the most easily accepted entry method; frameworks are the most natural competitive vehicle. These technical deployments may not immediately generate revenue but can seize opportunities in future order formation.

This also explains why, despite knowing scenarios haven't worked through yet, giants unanimously push frameworks to the forefront.

Open-sourcing is not the endpoint but a prelude to an entry point war.

**To B Real Value Has Not Yet Been Realized**

Despite everyone appearing busy, the liveliness of AI agent frameworks doesn't equal market maturity.

So far, no company can present large-scale enterprise cases proving that AI agents can indeed significantly reduce costs or improve efficiency. In fact, most applications remain in demonstration or pilot phases.

This means current competition is more of a "discourse power game" rather than commercial realization. The news effect brought by AI agent framework releases may even be more valuable than real applications.

Giants know well that no one dares assert that AI agents will definitely become the next entry point like mobile applications. But in a situation where risks and opportunities coexist, absence equals abandonment.

Therefore, the current AI agent framework boom is more like strategic defense—first secure positions, then see if the market will develop demand.

So this is actually both preparation for rainy days and something that must be done.

It's worth noting that in the current AI discourse environment, "open-source" and "breaking records" are often viewed as breakthroughs.

But from actual circumstances, open-sourcing is more like an attitude: merely conveying a signal of "we are present," but doesn't automatically mean industrial value.

For developers, Youtu-Agent's significance is providing an experimental field for quickly testing applications; but Tencent is likely more concerned about which scenarios truly have conversion value.

Whether Tencent's framework can continue evolving depends on two factors: first, whether Tencent is willing to maintain long-term investment in the community; second, whether it can fill gaps in industrial aspects like cloud computing, security, and compliance.

Without these two conditions, Youtu-Agent may become a "silent project" after a wave of attention.

Alibaba bets on platformization, Tencent explores specific scenarios, while Microsoft enters through applications (actually Alibaba has similar approaches, such as DingTalk, similar to Microsoft's Copilot, even more comprehensive and deeply embedded); three paths each have their logic but converge on the same track: AI agent frameworks may be future new entry points.

Let me briefly explain the similarities and differences between DingTalk and Copilot: Microsoft and Alibaba are actually both pursuing "dual-track advancement": on one hand is the framework layer (Microsoft's AutoGen, Alibaba's AgentScope), providing developers with capabilities to build complex agents; on the other hand is the application/scenario layer (Microsoft's Copilot, Alibaba's DingTalk AI Organization), directly embedding AI into existing workflows so enterprise users and ordinary employees can immediately perceive it.

The difference is: Microsoft leans toward "entering through tools to build reputation," with AutoGen being exploration at the developer layer and Copilot being implementation in productivity scenarios, relying on Office's user base to naturally push AI to users.

Alibaba takes a "two-pronged approach," with AgentScope attempting to build platform standards while AI organization experiments on DingTalk essentially doing "embedded applications" similar to Microsoft, but targeting internal management and collaboration methods within enterprises.

That is, Microsoft and Alibaba's strategies are not entirely different but two interpretations of the same logic: securing potential standards at the framework layer while cultivating real demand at the application layer.

It's just that Microsoft relies on globalized office suites while Alibaba depends on DingTalk's penetration rate among domestic enterprises.

Returning to the original question, giants' secret battle over AI agent framework entry points has not yet been validated at scale. Giants choose open-sourcing to disperse risks and to seize potential dominance.

The real significance of open-source AI agent frameworks may not lie in current technical functions but in who can bind more developers and applications to their ecosystem before future standards form.

From this perspective, this competition is far from settled. The current open-source boom is just the prelude to a new round of major battles.