Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents智能体编排：企业 AI 组织面临的是部署问题，而非平台问题——而且大多数企业只是把聊天机器人称为“智能体”

Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception.在 101 家企业中，智能体编排正向模型提供商平台整合——Anthropic 的 Claude 以巨大优势领先——企业选择这些平台是看重底层模型的“引力”，并以可靠的多步执行能力作为评判标准。但目前的雄心远超现实：大多数已部署的“智能体”仍只是聊天机器人的外壳，企业所期待的控制平面刻意保持混合架构以避免厂商锁定，且对 Token 消耗进行实时财务控制的情况依然罕见。

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.本期 VentureBeat Pulse 研究考察了企业智能体编排：企业在哪些平台上运行、驱动选择的因素是什么、他们优化什么目标、他们期望如何构建智能体控制架构，以及最能说明问题的一点——他们已部署的“智能体”编排程度究竟如何，以及他们对运行成本的控制有多严格。

The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.核心发现是编排雄心与编排现实之间的差距。企业正迅速向主流模型平台整合：Anthropic 的 Claude 是 40% 企业的首选平台，是竞争对手的两倍多，其次是微软（18%）和 OpenAI（13%）。选择的驱动力在于“模型引力”——即与最先进的基础模型原生对齐（21%），而成功的评判标准则是可靠的多步执行（任务完成可靠性占 32%，多步工作流管理占 28%）。然而，当被要求诚实评估其智能体组合时，71% 的受访者表示，他们部署的“智能体”中，只有四分之一或更少是真正的多步编排工作流，而非单次提示词的聊天机器人外壳；仅有 10% 的企业超过了半数。编排层的发展远超其所要运行的编排组合。

That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.这种差距塑造了企业正在部署的架构。到 2026 年底，绝大多数企业（51%）预计将采用混合控制平面（即提供商原生加外部编排），仅有 6% 的企业期望将控制权完全交给提供商管理的托管服务，因为厂商锁定（35%）是他们如果将控制权置于模型提供商内部时最担心的风险。投资紧随建设步伐：智能体工作流工具支出领先（34%），安全和权限执行（25%）紧随其后。而财务控制则全程滞后——超过四分之一（27%）的企业没有实时手段在账单产生前阻止失控的智能体。

Methodology研究方法

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.VentureBeat 作为其 Pulse Research 系列的一部分进行了这项调查，本次工具专注于企业智能体编排。回复经过筛选，涵盖员工人数在 100 人及以上的组织（n=101），数据取自 2026 年 6 月的一次调研；由于这是一次性调研而非跨月汇总样本，报告结果为横截面分析，不推断月度趋势。

By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).按组织规模划分，样本均匀分布在各企业区间：100–499 人、2,500–9,999 人和 50,000+ 人（各占 21%），10,000–49,999 人和 500–2,499 人（各占 19%）。按角色划分，受访者均为资深且具备购买决策影响力的人员：产品和项目经理（15%）、CIO/CTO/CISO（13%）、顾问（13%），以及各类数据、AI 和工程总监及副总裁，其他职能占 18%。在采购方面，81% 的受访者是 AI 解决方案的推荐者、影响者或最终决策者（66% 为推荐者/影响者，15% 为最终决策者）。技术/软件行业占比最大（44%），其次是金融服务业（17%）和医疗保健/生命科学业（8%）。

At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.101 个样本量足以以合理的置信度进行方向性解读，尽管它仍属于自选样本，而非概率样本。

Finding 1: Orchestration runs on model-provider platforms发现 1：编排运行在模型提供商平台上

Anthropic’s Claude leads; open frameworks are marginalAnthropic 的 Claude 领先；开源框架处于边缘地位

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.我们询问了企业目前主要使用哪种智能体编排平台。答案集中在主流模型提供商身上，且特别集中于其中一家。

A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.关于这些份额的解读说明：如方法论部分所述，受访者为自选，且该问题要求他们选择单一主要平台，因此这些数据衡量的是在 AI 活跃的技术决策者这一自选群体中，哪个平台在每个企业的部署中处于领先地位。以此方式构建的样本可能与基于支出的市场衡量标准有很大出入，且每项 VB Pulse 调查都有其独特的样本和公司规模组合，因此不应在我们的不同调查之间比较厂商数据。请将这些份额视为该群体当前主要编排投入的写照，而非市场份额。

The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.模型平台占据主导地位。Anthropic、微软、OpenAI、Google 和亚马逊合计占部署量的 80% 左右（101 家中的 81 家），而技术讨论中常见的开源框架（LangChain/LangGraph）和内部定制构建仅占个位数。Anthropic 的领先地位（40%，是下一平台的两倍多）反映了“发现 2”中提到的“模型引力”选择逻辑：企业正在选择与他们想要构建的模型相配套的编排层。正如之前智能体安全调研中的安全厂商一样，在技术圈定义品类的工具尚未成为企业部署的集中地。另有 3% 的企业根本没有进行编排。

Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.受访者对他们运行的平台总体评分为 3.94 分（满分 5 分，109 人回答），其中“性价比”评分为 3.94 分，“实施简易度”得分最低，为 3.85 分——这使得编排在我们的五项指标满意度中排名垫底，仅高于评估工具。考虑到 96% 的用户计划在一年内改变其编排方案，不到 4 分的评分应被视为一种“临时接受”：平台目前运行尚可，但还不足以让他们停止寻找更好的方案。这些评分与几乎一致的变更意向并存；这是一种企业更多是忍受而非热爱的技术层。

Finding 2: Model gravity drives platform selection发现 2：模型引力驱动平台选择

The base model, not the tooling, decides the platform决定平台的不是工具，而是基础模型

We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.我们询问了什么最影响编排平台的选择。最大的单一因素是底层模型的吸引力，尽管灵活性和开发简易度也紧随其后。

Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.模型引力领先是解释 Anthropic 平台领先的选择侧原因：企业选择了与他们已标准化的前沿模型最接近的编排环境。但后续的因素使情况变得复杂——跨模型和工具的灵活性（17%）以及开发简易度（17%）表明企业也希望避免被该选择所困，这预示了“发现 6”中提到的锁定恐惧。安全和权限（14%）以及总拥有成本（11%）完善了务实的购买逻辑。性能（延迟/内存）以 4% 的比例位居最后，这提醒我们，在采用的现阶段，核心约束是模型契合度和选择权，而非单纯的速度。

Finding 3: The job is reliable multi-step execution发现 3：核心任务是可靠的多步执行

Enterprises just orchestration by whether it completes the work企业通过工作完成度来评判编排

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.我们询问了企业优化什么——即他们衡量编排成功的主要指标。可靠性和多步工作流管理占据主导地位；开发者和用户导向的指标则排在后面。

Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.任务完成可靠性（32%）和多步工作流管理（28%）合计占回复的 59%（101 家中的 60 家）：在企业看来，当编排能够可靠地引导任务完成多个步骤直至最终达成时，它就是成功的。开发者生产力（17%）很重要但属于次要因素——这与框架讨论中其显著地位相反；而终端用户体验（9%）是一个较小的关注点，这与编排是内部执行问题而非用户体验问题的一致。这种“可靠性优先”的标准正是“聊天机器人陷阱”这一发现如此尖锐的原因：企业将成功定义为可靠的多步执行，然而他们大多数已部署的“智能体”根本还做不到多步工作。

The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.这种陷阱并非均匀分布。按组织规模划分样本，77% 的小型企业表示其智能体中只有四分之一或更少能完成真正的多步工作，而大型企业中这一比例为 62%。大型企业在真正的多步部署方面走得更远；聊天机器人陷阱在方向上更倾向于中型市场。

Finding 4: Consolidate, productionize, and build in-house 发现 4：整合、生产化和内部构建

Three strategic moves are nearly tied for the year ahead未来一年的三大战略举措几乎并列

We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.我们询问了企业预计未来 12 个月内其编排策略会有什么重大变化。排名前三的举措分布均匀，几乎不分伯仲。

The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.前三名——构建内部控制（25%）、标准化单一框架（24%）以及将智能体从沙盒移至生产环境（23%）——在统计上难以区分，它们讲述了一个故事：企业正从实验阶段转向运营整合。他们想要更少的框架、更多的生产部署以及对控制层更多的所有权；只有 4% 的企业预计不会有任何变化。对定制内部控制平面的渴望与“发现 1”中的平台集中度并存值得注意——企业正标准化于模型提供商平台，同时计划用他们拥有的控制逻辑来包装它们，这正是“发现 6”中明确指出的混合立场。

Finding 5: Investment flows to workflow tooling发现 5：投资流向工作流工具

Tooling and permissions lead the spend; monitoring trails工具和权限占据支出领先地位；监控则滞后

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.我们询问了明年哪项与编排相关的投资增长最快。智能体工作流工具领先，安全和权限执行紧随其后。

Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.工作流工具领先（34%）是“发现 3”中可靠性和多步执行优先级在预算层面的体现：资金正流向那些能够可靠地串联步骤的机制。安全和权限执行（25%）以及扩展基础设施（20%）紧随其后——这是将智能体从沙盒带入生产环境所需的投资，也是“发现 4”中的战略举措。监控和调试仅占 11%，另有 11% 的企业表示预算持平。在工具、权限和扩展性上投入的权重高于纯粹的可观测性，这表明企业正在投入建设和加固编排，而不仅仅是监控其运行。

Finding 6: The control plane will be hybrid — and lock-in is why发现 6：控制平面将是混合式的——原因在于锁定风险

Enterprises expect to split control between providers and their own layer企业期望在提供商和自身控制层之间分配控制权

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.我们询问了企业期望到 2026 年底智能体的主要控制平面位于何处，以及如果控制权位于模型提供商平台内，他们最担心什么。绝大多数企业期望采用混合模式——而原因正是厂商锁定。

Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.混合控制是绝大多数企业的主流预期（51%），仅有 6% 的企业期望将控制权完全交给提供商管理的托管服务。综合来看，混合、自定义和外部抽象选项——即所有至少部分将控制权置于提供商之外的架构——总计占 88%（101 家中的 89 家）。当我们询问关于驻留在提供商内部控制的风险时，原因直接浮现：厂商锁定以 35%（101 家中的 35 家）位居榜首，领先于安全和权限限制（28%）以及跨模型和工具的不灵活性（21%）。这种模式呼应了上一波调研中“不要相信模型能自我监管”的立场——在这里，企业会在提供商平台上构建，但拒绝完全受其支配。混合控制平面是他们针对最担心的锁定风险所采取的架构对冲。

The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.6 月份的数据显示出对混合控制平面的偏好，这标志着与早期的变化。在 4 月至 5 月的调查（n=145）中，只有 34% 的企业期望混合控制平面，而有更多的人（12%）期望将控制权完全交给提供商管理的托管服务。这两个时间点的数据尚不能衡量确定的长期趋势，但对话的方向是明确的：即保持控制权。

Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.锁定也成为一个新的主要担忧。在 4 月至 5 月的调研中，主要担忧是安全和权限限制（32%），锁定排在第二位（24%）；到了 6 月，两者位置互换。对提供商平台的担忧似乎正在从“能否确保安全”演变为“能否被替换”。

Finding 7: The chatbot trap — most “agents” aren’t agents yet发现 7：聊天机器人陷阱——大多数“智能体”还不是真正的智能体

Enterprises admit most deployments are still chatbot wrappers企业承认大多数部署仍是聊天机器人外壳

We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.我们要求企业诚实评估其组合：已部署的“智能体”中，有多少是真正的多步编排工作流，有多少是简单的单次提示词聊天机器人外壳。答案是本期调研的决定性发现。

This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.这是本报告核心的差距所在。结合后两个区间，71% 的企业（101 家中的 72 家）表示其部署的“智能体”中只有四分之一或更少是真正经过编排的——仅有 10%（101 家中的 10 家）超过了半数。早期发现中记录的雄心——模型提供商平台、可靠性优先的成功指标、生产 rollout、刻意的控制架构——远超已部署的现实，而现实中绝大多数仍是伪装成智能体的单次提示词助手。这与其说是矛盾，不如说是一张路线图：之所以部署平台、预算和策略，正是因为编排组合依然非常薄弱。后续调研的问题在于，现实追赶雄心的速度有多快。

Finding 8: Fiscal control is still reactive发现 8：财务控制仍是被动的

Only a minority can stop a runaway agent before the bill arrives只有少数企业能在账单到来前阻止失控的智能体

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.最后，我们询问了企业如何对智能体 Token 消耗实施财务控制——即自主循环在干预前耗尽预算的风险。大多数企业依赖原生上限或事后监控；实时的程序化控制依然是例外。

More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.超过四分之一的企业（27%）承认，他们没有实时的、程序化的方法在账单超支前阻止智能体——他们事后才从日志中得知。另有 32% 的企业完全依赖主要平台内置的原生上限和限流，这种控制的好坏取决于提供商的工具，这也与“发现 6”中提到的锁定担忧相关。那些构建自定义网关（23%）或利用跨模型路由进行成本套利（19%）的企业，才是将 Token 消耗视为工程问题并进行确定性控制的企业。与编排成熟度一样，财务控制是运营现实落后于雄心的领域：智能体进入生产环境的速度快于其周边成本控制平面的构建速度。

It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.值得注意的是，根据公司规模出现了分化：约三分之一员工人数在 2,500 人以下的企业（34%）仅对智能体支出进行被动控制，而大型企业中这一比例为 20%——这些数据虽具方向性，但与聊天机器人陷阱的分化一致。中型市场正在使用最不成熟的智能体，且预算监控手段也最匮乏。

The bottom line: The layer is real; most of the agents aren't yet结论：编排层是真实的；但大多数智能体还不是

Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing on model-provider platforms — Anthropic’s Claude leads at 40% — chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most.员工人数在 100 人及以上的组织所描述的编排策略正在快速整合，但成熟缓慢。他们正标准化于模型提供商平台——Anthropic 的 Claude 以 40% 领先——这是基于底层模型的引力选择的，他们以可靠的多步执行来评判成功。投资正流向工作流工具和权限，策略是整合框架并将智能体推向生产环境，他们期望的控制平面刻意保持混合架构，因为厂商锁定是他们最担心的风险。

But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed “agents” are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The question for subsequent waves is whether the deployed reality closes the gap on the ambition — or whether the chatbot trap proves stickier than the roadmap assumes.但诚实的自我评估打破了雄心。71% 的企业表示其部署的“智能体”中只有四分之一或更少是真正经过编排的，只有 10% 超过了半数，且超过四分之一的企业无法实时阻止失控的智能体。编排层——平台、预算、控制架构——的发展超前于其所要运行的编排组合。对于 6 月份单次调研的 101 名受访者而言，这更像是一个明确的方向信号，而非精确的测量：企业在大多数智能体还没做任何需要编排层的工作之前，就已经决定了他们想要如何编排智能体。后续调研的问题在于，已部署的现实能否赶上雄心——或者聊天机器人陷阱是否比路线图设想的更顽固。

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.基于 101 名合格企业受访者（100+ 员工）的调查回复，取自 2026 年 6 月的单次调研。由于这是一次性调研而非跨月汇总样本，结果为方向性解读而非确定的趋势。受访者包括产品和项目经理、CIO、CTO 和 CISO、顾问、以及数据、AI 和工程领域的总监及副总裁，涵盖技术/软件、金融服务、医疗保健及其他行业。