智能体编排：企业AI组织面临部署难题，而非平台难题——多数所谓的“智能体”实为聊天机器人包装

在101家企业中，智能体编排正在向模型提供商平台整合——Anthropic的Claude以大幅优势领先——选择受“模型引力”驱动，评判标准是可靠的多步骤执行。但雄心远超前于现实：大多数部署的“智能体”仍是聊天机器人包装，企业期望的控制平面刻意采用混合模式以避免锁定，而对token消耗的实时财务控制仍是例外。

本轮VentureBeat Pulse研究考察企业智能体编排：企业运行于哪些平台、驱动选择的原因、优化目标、期望的智能体控制结构，以及——最具揭示性的——其部署的“智能体”究竟有多少真正实现了编排，以及他们对运行成本的控制有多严格。

核心发现是编排雄心与编排现实之间的差距。企业正快速整合到主要模型平台：Anthropic的Claude是40%企业的主要平台，是任何竞争对手的两倍以上，其次是Microsoft（18%）和OpenAI（13%）。选择受“模型引力”驱动——与最先进基础模型的原生对齐（21%）——成功的评判标准是可靠的多步骤执行（任务完成可靠性32%，多步骤工作流管理28%）。然而，当被要求诚实地评估其投资组合时，71%的企业表示其部署的“智能体”中有四分之一或更少是真正的多步骤编排工作流，而非单提示的聊天机器人包装，仅有10%的企业跨过了半数。编排层的建设远早于它所要运行的编排化投资组合。

这一差距塑造了企业正在建立的基础架构。到2026年底，明显多数（51%）期望采用混合控制平面——提供商原生加外部编排——只有6%期望将控制权完全交给提供商管理的服务，因为如果控制权留在模型提供商内部，厂商锁定（35%）是他们最担心的风险。投资紧随建设步伐：智能体工作流工具引领支出（34%），安全与权限执行（25%）紧随其后。而财务控制在整个过程中滞后——超过四分之一（27%）的企业无法在账单到来之前实时阻止失控的智能体。

方法学

VentureBeat在其持续进行的Pulse研究系列中实施了这项调查，本问卷专注于企业智能体编排。受访者筛选为员工人数100人以上的组织（n=101），数据来自2026年6月的单一波次；由于这是一次横截面调查而非多月的合并样本，报告以横截面方式解读，不推断月度环比趋势。

按组织规模划分，样本在各企业区间均匀分布：100–499名员工、2,500–9,999名和50,000名以上各占21%，10,000–49,999名和500–2,499名各占19%。按角色划分，样本资深且具有购买可信度：产品与项目经理（15%）、CIO/CTO/CISO（13%）、顾问与咨询师（13%），以及各数据、AI和工程主管与副总裁，另有“其他”职能占18%。在采购方面，81%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/软件是最大的行业，占44%，其次是金融服务（17%）和医疗保健/生命科学（8%）。

在101名受访者下，样本足以方向性地提供合理置信的解读，尽管它仍是自选样本而非概率样本。

发现1：编排运行在模型提供商平台上

Anthropic的Claude领先；开放框架边缘化

我们询问了企业目前主要使用哪个智能体编排平台。答案集中在主要模型提供商上——且特别集中在某一平台上。

40%

使用Anthropic的Claude平台与Agent Skills——是任何竞争对手的两倍以上

18%

使用Microsoft AI Foundry / Copilot Studio

13%

使用OpenAI的Agents SDK / Responses API

8%

使用Google的Enterprise Agent Platform——另有2%使用Amazon Bedrock Agents

6%

使用LangChain / LangGraph——5%构建自定义内部方案，3%尚未进行编排

关于这些份额的解读说明。如方法学部分所述，受访者为自选样本，且该问题要求他们选定一个主要平台——因此数据衡量的是每个企业部署中哪个平台占主导，受众是AI活跃的技术决策者自选群体。这种构建方式的样本可能与按支出加权的市场度量有显著差异，且每次VB Pulse调查抽取的样本及其公司规模组合不同，因此供应商数据也不应跨调查比较。请将这些份额视为该群体当前主要编排赌注的写照，而非市场份额。

模型平台占据主导。Anthropic、Microsoft、OpenAI、Google和Amazon合计约占总部署的80%（101家中的81家），而支撑工程讨论的开放框架（LangChain/LangGraph）和自定义内部构建份额仅有个位数。Anthropic的领先——40%，是下一个平台的两倍以上——反映了发现2中的“模型引力”选择逻辑：企业正在选择他们希望构建的模型所附带的编排层。与先前智能体安全浪潮中的安全厂商情况一致，在技术圈定义类别工具尚未成为企业部署的集中地。仅有3%的企业完全没有进行任何编排。

受访者对所运行平台的总体评分为5分制的3.94分（109人作答），其中“性价比”恰好也是3.94分，而“实施便利性”得分最低，为3.85分——这使得编排在我们的五项跟踪满意度排名中接近底部，仅高于评估工具。一个接近4分（满分5分）的评分，且使用者中96%计划在一年内改变其编排方式，这可以解读为暂时接受：平台运行现状足够好，但不足以阻止寻找更好选择。评分与几乎普遍的改变意愿并存；这是企业容忍多于喜爱的层面。

发现2：模型引力驱动平台选择

决定平台的是基础模型，而非工具

我们询问了什么对编排平台选择影响最大。最大单一因素是基础模型的吸引力——尽管灵活性和开发易用性紧随其后。

21% 认为是模型引力——与最先进基础模型的原生对齐 | 17% 认为是跨模型和工具的灵活性 | 17% 认为是易于开发 | 14% 认为是安全与权限 | 11% 认为是总体拥有成本——10% 认为是智能体执行控制

模型引力居首，从选择侧解释了Anthropic的平台领先地位：企业选择与他们已标准化的前沿模型最接近的编排环境。但紧随其后的层次使图景复杂化——跨模型和工具的灵活性（17%）和开发易用性（17%）表明，企业也希望避免被该选择所束缚，这预示了发现6中的锁定恐惧。安全与权限（14%）和总体拥有成本（11%）完善了务实的购买逻辑。性能（延迟/内存）排在最后，仅占4%，这提醒我们，在此采用阶段，约束瓶颈是模型匹配和可选择性，而非原始速度。

发现3：任务是可靠的多步骤执行

企业评判编排的标准在于它能否完成工作

我们询问了企业优化什么——他们编排的主要成功指标。可靠性和多步骤工作流管理占主导；面向开发者和用户的指标次之。

32% 认为是任务完成可靠性——领先的成功指标 | 28% 认为是多步骤工作流管理 | 17% 认为是开发者生产力 | 9% 认为是最终用户体验 | 9% 认为是运营稳定性

任务完成可靠性（32%）和多步骤工作流管理（28%）合计占响应的59%（101家中的60家）：在企业看来，编排成功意味着可靠地通过多个步骤将任务执行到底。开发者生产力（17%）重要但属次要——与框架讨论中的突出程度相反——最终用户体验（9%）是次要关切，这与编排是内部执行问题而非用户体验问题一致。这种可靠性优先的标准，恰恰让聊天机器人陷阱这一发现显得格外尖锐：企业将成功定义为可靠的多步骤执行，但他们部署的大多数“智能体”根本尚未执行多步骤工作。

这一陷阱分布并不均匀。按组织规模切分样本，较小企业中77%表示其智能体中四分之一或更少是真正的多步骤工作，而较大企业为62%。较大企业在真正的多步骤部署上走得明显更远；聊天机器人陷阱在方向上是一种中端市场状况。

发现4：整合、产品化与内部构建

未来一年的三个战略举措几乎势均力敌

我们询问了企业未来12个月内编排策略中预期的主要变化。三项举措集中在顶部，几乎平分秋色。

25% 将增加对自定义内部编排控制平面的投资 | 24% 将标准化为单一集中式框架 | 23% 将把智能体从沙盒扩展到生产环境 | 9% 将转向交钥匙的、原生嵌入式架构 | 8% 预期模型原生自主性或外部框架——各占一半

前三项——内部控制构建（25%）、标准化为单一框架（24%），以及将智能体从沙盒移向生产（23%）——在统计上无显著差异，诉说着同一个故事：企业正从实验转向运营整合。他们想要更少的框架、更多的生产暴露，以及对控制层更多所有权；只有4%预期无变化。对自定义内部控制平面的需求与发现1中的平台集中化并行不悖——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进行包裹，这正是发现6所明确的混合姿态。

发现5：投资流向工作流工具

工具与权限领跑支出；监控落后

我们询问了明年哪项与编排相关的投资将增长最多。智能体工作流工具领跑，安全与权限执行紧随其后。

34% 选择智能体工作流工具——头号增长领域 | 25% 选择安全与权限执行 | 20% 选择扩展智能体的基础设施 | 11% 选择智能体监控与调试 | 11% 报告预算持平——预期无增长

工作流工具领先（34%）是发现3中可靠性与多步骤优先级的预算侧体现：资金流向能够可靠串联步骤的机械。安全与权限执行（25%）和扩展基础设施（20%）紧随其后——这些都是将智能体从沙盒推向生产所需的投资，也是发现4中的战略举措。监控和调试仅占较小的11%，另有11%报告预算持平。与纯可观测性相比，工具、权限和扩展的权重表明，企业正在投入以构建和强化编排，而不仅仅是观看其运行。

发现6：控制平面将是混合化的——锁定是原因

企业期望在提供商与自有层之间分割控制权

我们询问了企业预计到2026年底智能体的主要控制平面将位于何处，以及如果控制权留在模型提供商平台内部，他们最担忧什么。明显多数期待混合模式——而厂商锁定是原因所在。

51% 期望混合控制平面——提供商原生加外部编排 | 22% 期望自定义内部控制平面 | 15% 期望从模型提供商抽象出的外部平台 | 6% 期望提供商托管的智能体服务 | 6% 不期望大规模部署自主智能体

混合控制以较大优势成为主流期望（51%），只有6%期望完全将控制权交给提供商托管服务。综合来看，混合、自定义和外部抽象选项——所有至少部分将控制权保持在提供商之外的架构——合计占88%（101家中的89家）。原因直接在我们询问提供商驻留控制风险时显现：厂商锁定以35%（101家中的35家）居首，领先于安全与权限限制（28%）和跨模型与工具的灵活性不足（21%）。这一模式与先前浪潮中“不信赖模型自我监管”的姿态一致——在这里，企业将在提供商平台上构建，但拒绝被其完全统治。混合控制平面是面对他们最担心的锁定风险时的架构对冲。

六月数据中对混合控制平面的偏好标志着较之前的变动。在四至五月调查（n=145）中，只有34%期望混合控制平面，更多人（12%）期望将控制权完全交给提供商托管服务。这两个快照尚未构成确认的纵向趋势——但对话方向是明确的：走向保持控制。

锁定风险也作为一个首要关切新近出现。在四至五月浪潮中，首要关切是安全

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.

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.

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.

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.

Methodology

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.

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%).

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.

Finding 1: Orchestration runs on model-provider platforms

Anthropic’s Claude leads; open frameworks are marginal

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

Finding 1 — Orchestration runs on model-provider platforms

40%

use Anthropic's Claude Platform & Agent Skills — more than double any rival platform

18%

use Microsoft AI Foundry / Copilot Studio

13%

use OpenAI's Agents SDK / Responses API

8%

use Google's Enterprise Agent Platform — plus 2% on Amazon Bedrock Agents

6%

use LangChain / LangGraph — 5% build custom in-house, 3% aren't orchestrating yet

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.

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.

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.

Finding 2: Model gravity drives platform selection

The base model, not the tooling, decides the platform

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.

Finding 2 — Model gravity drives platform selection

21% name Model Gravity — native alignment with a state-of-the-art base model	17% name flexibility across models and tools	17% name ease of development	14% name security and permissions	11% name Total Cost of Ownership — 10% cite control over agent execution

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.

Finding 3: The job is reliable multi-step execution

Enterprises just orchestration by whether it completes the work

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.

Finding 3 — The job is reliable multi-step execution

32% name task completion reliability — the leading success metric	28% name multi-step workflow management	17% name developer productivity	9% name end-user experience	9% name operational stability

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.

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.

Finding 4: Consolidate, productionize, and build in-house

Three strategic moves are nearly tied for the year ahead

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.

Finding 4 — Consolidate, productionize, and build in-house

25% will increase investment in custom, in-house orchestration control planes	24% will standardize on a single centralized framework	23% will expand agents from sandbox into production	9% will shift toward turnkey, natively embedded architectures	8% expect model-native autonomy or external frameworks — split evenly

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.

Finding 5: Investment flows to workflow tooling

Tooling and permissions lead the spend; monitoring trails

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

Finding 5 — Investment flows to workflow tooling

34% name agent workflow tooling — the top growth area	25% name security and permissions enforcement	20% name infrastructure for scaling agents	11% name agent monitoring and debugging	11% report flat budgets — no increase expected

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.

Finding 6: The control plane will be hybrid — and lock-in is why

Enterprises expect to split control between providers and their own layer

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.

Finding 6 — The control plane will be hybrid

51% expect a hybrid control plane — provider-native plus external orchestration	22% expect a custom in-house control plane	15% expect external platforms abstracted from model providers	6% expect a provider-managed agent service	6% do not expect to deploy autonomous agents at scale

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.

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.

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.

Finding 7: The chatbot trap — most “agents” aren’t agents yet

Enterprises admit most deployments are still chatbot wrappers

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.

Finding 7 — The chatbot trap

62% say only 1–25% are true orchestration — most are basic assistants	19% say 26–50% have moved to stateful, orchestrated architectures	9% say 0% — every deployment is a chatbot or prompt wrapper	7% say 51–75% are complex, multi-agent pipelines	3% say 76–100% — advanced, largely autonomous systems

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.

Finding 8: Fiscal control is still reactive

Only a minority can stop a runaway agent before the bill arrives

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.

Finding 8 — Fiscal control is still reactive

32% rely on native platform controls — built-in budget caps and throttling	27% have reactive monitoring only — no real-time kill switch	23% build custom gateway plumbing — proxy middleware to intercept runaway runs	19% use dynamic routing arbitrage — offload heavy work to low-cost models

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.

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.

The bottom line: The layer is real; most of the agents aren't yet

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.

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.

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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.