在 157 家企业中，组织正在授予 AI 代理更多自主权，同时却对用来把关的评估信任度在降低。半数企业已经将某个通过了内部评估的代理部署上线，却在客户面前遭遇失败；仅二十分之一的企业完全信任自动化评估；而被提及最多的弱点是评估结果与现实结果不相符。然而，三分之二的企业已经允许或正在积极构建仅凭自动化评估就将代理变更部署到生产环境的流程——无需人工把关。其结果便是评估差距——即企业赋予代理的自主权与他们信任本应捕捉故障的测试之间的落差。

本波 VentureBeat Pulse Research 调查探讨了技术领导者如何衡量代理性能：他们使用哪些可靠性和评估平台，如何选择和信任这些平台，生产环境中什么会出问题，以及他们愿意在多大程度上让代理在没有人工干预的情况下运行。

核心发现是评估差距——即企业授予代理的自主权与他们信任用来管控自主权的评估之间的距离。半数组织（50%）在过去一年内部署过通过内部评估但随后引发客户事故的代理或 LLM 功能，四分之一的企业不止一次遇到这种情况。对测试本身的信任度很低：仅 5% 表示完全信任当下的自动化评估，而被提及最多的一项局限是评估与现实结果对齐不佳（29%）。企业发现，评估通过并不等于代理能正常工作。

让差距变得意义重大的是发展方向。三分之二的组织（66%）已经允许对低风险代理进行完全自动化、零人工介入的部署（34%），或者正在积极设计管线以便在十二个月内实现这一能力（33%）。与此同时，理当赢取这种信任的评估技术栈呈碎片化且不成熟：最常用的主要工具是模型提供方的原生评估，与没有任何专用工具并列（各占 17%）；仅约四分之一的企业对在线生产流量执行实时质量检查。自主权的到来速度超过了保障的到位速度。

调查方法

VentureBeat 将本次调查作为其持续进行的 Pulse Research 系列的一部分，这份名为“代理可靠性与评估追踪”的调查聚焦技术领导者如何评估代理性能与可靠性。回答筛选自员工数在 100 人及以上的组织（n=157），数据来源于 2026 年 6 月的一次调查；由于这是单期而非跨多月混合样本，报告以横截面方式解读，不推断月间趋势。对于多选题，各项比例之和可能超过 100%。

从角色来看，样本群体资深且具有采购可信度：38% 是 AI 采购的最终决策者，另有 34% 是推荐者或影响者。产品与项目经理（15%）、顾问与咨询师（10%）、工程/IT 总监（8%）、CIO/CTO/CISO（8%）在具名职位中位居前列，同时还有一个较大的“其他”职能群体（37%）。组织规模以中端市场为主：100–499 人（37%）和 500–2,499 人（27%）的企业占比最高，2,500–9,999 人（20%）、10,000–49,999 人（10%）和 50,000 人以上（6%）的群体依次居后。科技/软件是最大的行业，占 23%，其次是零售/消费（15%）、医疗保健/生命科学（12%）和制造业（10%）。

样本量为 157 个回复，足够解读方向性信号，但应视为方向性参考而非精确测量；样本属于自选参与，并非概率样本。它偏向中端市场，因此最好将其解读为来自正在积极建立代理评估实践的组织视角，而非来自最大规模的运营者。

注：本次调查是针对六月期从早前的“LLM 可观测性与评估”调查重建而来；由于问题和样本不同，不与四月至五月的数据进行比较。

发现 1：评估通过并不代表代理能正常工作

半数企业曾部署评估通过但随后导致客户失败的代理

我们询问，在过去 12 个月内，组织是否部署过通过其内部评估但随后导致客户事故的代理或 LLM 功能。在运行评估的组织中，有一半回答是肯定的。

发现 1——评估通过并不代表代理能正常工作

• 24% 表示肯定，不止一次——评估与现实之间反复出现的差距
• 36% 未发现此类生产故障
• 8% 不进行部署前评估；另有 6% 未追踪此类情况或不知情

这是本报告的决定性数据。半数组织（50%）曾将其自认为通过内部评估的 AI 功能上线，然后在客户面前失败——输出错误、工作流中断或出现质量事故——四分之一的企业不止一次经历过。仅有 36% 报告无此类失败，其余的组织要么不进行部署前评估（8%），要么未对根本原因进行足够追溯而无法判断（6%）。失败精确且代价高昂：评估说代理已就绪，但事实并非如此。后续的一切——企业如何信任其评估、监控哪些内容以及授予多少自主权——都受此经历塑造。

发现 2：几乎无人完全信任自动化评估

首要抱怨：评估与现实结果不符

我们询问，目前最削弱对自动化代理评估信任的局限是什么。仅有极少数企业毫无抱怨。

发现 2——几乎无人完全信任自动化评估

• 29% 与现实结果对齐不佳——最主要的局限
• 21% 评估偏差或不一致
• 18% 缺乏可解释性
• 17% 数据泄露或隐私问题
• 11% 工具不成熟；仅 5% 表示完全信任当下的自动化评估

对自动化评估的信任十分稀缺且具体。仅有 5% 的组织表示完全信任自动化评估的现状——这意味着 95% 指出了制约他们的局限。最常见的一条（占 29%）最直接地解释了发现 1：评估与现实结果对齐不佳，让代理通过测试却在后来失败。偏差或不一致（21%）和缺乏可解释性（18%）紧随其后——企业往往无法判断评估为何得出该结论——另有 17% 指出评估过程中存在数据泄露或隐私问题。用于认证代理的测试尚未被信任能够认证它们，这也正是发现 3 中的自主权轨迹如此引人注目的原因。

发现 3：自主权上限仍在上升

三分之二的企业已允许或正在构建零人工介入的部署

我们询问，组织是否愿意让自主代理仅基于自动化评估结果就将代码或系统变更部署到生产环境，而无需任何人工校验。这一轨迹直接穿越了信任缺口。

发现 3——自主权上限仍在上升

• 34% 已经允许，针对特定低风险代理或变更
• 33% 尚未允许，但正在积极设计管线以在 12 个月内实现
• 22% 不允许，且预计在可预见的未来不打算允许完全自动化部署
• 8% 不适用——他们不部署自主代理；3% 不知道

这是报告核心的悖论。尽管几乎无人完全信任自动化评估（发现 2），三分之二的组织（66%）要么已经允许对低风险代理进行零人工介入部署（34%），要么正在积极设计管线以在一年内实现（33%）。仅 22% 排除了可预见未来内的可能性。方向毫不含糊：企业正在转向让评估自主为生产把关——移除人工检查——而与此同时他们却说这些评估无法可靠地匹配现实。自主权的天花板上升速度超过了其下的保障，这正是发现 1 中误信失败会蔓延而非缩小的机制。

值得注意的是，这一自主权赌注并不仅限于小公司。按公司规模拆分样本，大型企业比小型企业更接近无需人工审查的状态（70% 对比 64%），且更有可能部署过通过评估却在客户面前失败的代理（54% 对比 48%）。那种认为大型受监管机构会最久保留人工介入的假设，在此样本中恰恰相反。当然，这些只是方向性数据，因为调查并非大样本——来自 2,500 名员工以上公司的回复有 57 份，低于此规模的有 100 份。

发现 4：评估技术栈碎片化且由模型提供方主导

提供方原生评估领先——与无专用工具并列

我们询问企业目前主要使用哪种代理可靠性或评估平台。市场上没有明显的领导者——并且有很大一部分没有任何专用工具。

发现 4——评估技术栈碎片化且由提供方主导

• 17% 使用 OpenAI Developer Platform 原生评估/追踪——最常用的工具之一
• 17% 完全没有使用任何专用的代理可靠性或评估工具
• 13% 使用 Anthropic Claude Console 原生评估
• 12% 使用 Confident AI (DeepEval)；11% 使用自研内部工具
• 8% 使用 Braintrust；专门的评估厂商（LangSmith、Weave、Promptfoo、Langfuse、Arize）各占低个位数百分比

评估层尚处早期且尚未整合。提供方原生工具领先——OpenAI 的原生评估和追踪（17%）以及 Anthropic 的 Claude Console 评估（13%）合计超过任何独立平台——但却与一个引人注目的答案并列首位：17% 的企业完全未使用任何专用代理评估工具，这对将代理交付给客户的组织而言是一个显著的缺口。专业的评估厂商——DeepEval（12%）、Braintrust（8%）、LangSmith、Weave、Promptfoo、Langfuse、Arize——分散在个位数到低双位数之间，另有 11% 自行构建。尚无独立平台成为类别标准，这使得大多数企业使用提供方原生工具、自行编写的脚本或无任何工具来评估代理。

发现 5：生产监控鲜少关注输出质量

仅四分之一的企业对在线流量进行实时质量检查

AI 代理的生产监控可以关注两种截然不同的事物。它可以关注系统是否在运行——代理是否在线并响应，每个请求是否完成，多快，成本多少，是否有错误。或者它可以关注代理的输出是否正确——在每条输出发出时对其内容进行自动化评估：代理是否给出了正确的答案，采取了正确的行动，遵守了策略。两者的区别很关键，因为一个自信的错误答案对第一种监控是不可见的：请求完成，响应迅速，没有报错，所有运行指标看起来都健康。我们询问组织其在线生产监控目前构建的目的是哪一种。

发现 5——生产监控鲜少关注输出质量

• 25% 进行事务轨迹日志记录——基础设施跨度、令牌计数、原始 I/O 用于事后调试
• 25% 进行网关基础设施追踪——在 API 层监控延迟、错误率和成本，而非输出质量
• 23% 进行内联质量断言——在线流量上的实时评判/护栏，在质量下降时发出告警
• 10% 进行临时的在线输出质量审查；另有 17% 不知道或表示不属于其领域

从实际监控的内容来看，差距悬殊：51% 的组织仅监控代理是否在运行，而 23% 监控其回答是否正确。算上临时审查者和不知情者，约四分之三的组织在生产环境中没有对输出正确性进行自动化实时评估——它们能看到系统在运行及其成本，但回答的正确性却靠信任。这一盲点正是部署前差距在运行时的对应：正在将人工从部署决策中移除的那些组织，大多无法实时察觉已部署的代理何时开始出错。

发现 6：依照成本购买，看重一致性衡量

价格和集成度驱动选择；评估一致性是目标

我们询问了是什么最大程度影响企业选择评估供应商，以及他们将什么视为成功的主要衡量标准。两者都很务实。

发现 6——依照成本购买，看重一致性衡量

• 28% 选择供应商主要基于评估成本——首要选择因素
• 27% 基于集成便捷性；24% 基于评估准确性
• 36% 将评估一致性列为主要成功指标——最常被提及的目标
• 19% 以实验速度衡量成功；18% 以故障或回归的减少衡量

企业依据经济性购买评估工具，并依靠可重复性来建立信任。评估成本（28%）略领先于选择因素，紧随其后的是集成便捷性（27%）和评估准确性（24%）——可观测性广度（13%）和供应商路线图（4%）的重要性低得多。在成功长什么样方面，超过三分之一（36%）提到评估一致性——对相同行为每次都得到相同的结果——远高于实验速度（19%）、故障减少（18%）、生产可见性（13%）和合规性（11%）。对一致性的强调很能说明问题：在组织能够信任评估结果之前，他们需要其保持稳定——而这一属性的缺失（偏差和不一致性）正是发现 2 中排名靠前的信任限制。对当前工具集的满意度仅为中等，在五分制下整体满意度平均为 3.8 分，涵盖整体满意度、eas

Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures.

This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop.

The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent.

What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same time, the evaluation stack that would have to earn that trust is fragmented and immature: the most common primary tools are the model providers’ native evals, tied with having no dedicated tooling at all (17% each); and only about a quarter of enterprises run real-time quality checks on live production traffic. The autonomy is arriving faster than the assurance.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey — the Agentic Reliability & Evals tracker — focused on how technical leaders evaluate agent performance and reliability. Responses are filtered to organizations with 100 or more employees (n=157), drawn from a single survey in June 2026; 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. Where questions were multiple-select, those shares can sum to more than 100%.

By role the sample is senior and buyer-credible: 38% are final decision-makers for AI purchases and another 34% recommenders or influencers. Product and program managers (15%), consultants and advisors (10%), directors of engineering/IT (8%), and CIOs/CTOs/CISOs (8%) lead the named titles, alongside a large “Other” function (37%). By organization size the sample is mid-market-weighted: 100–499 (37%) and 500–2,499 (27%) employees lead, with 2,500–9,999 (20%), 10,000–49,999 (10%), and 50,000+ (6%) above them. Technology/Software is the largest industry at 23%, followed by Retail/Consumer (15%), Healthcare/Life Sciences (12%), and Manufacturing (10%).

At 157 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent evaluation practices rather than from the largest operators.

Note: This survey was rebuilt for the June wave from the earlier “LLM observability and evaluations” survey; because the questions and sample differ, no comparisons are made to the April–May data.

Finding 1: A passing eval is not a working agent

Half have shipped an agent that passed evals, then failed a customer

We asked whether, in the past 12 months, organizations had deployed an agent or LLM feature that passed their internal evaluations but then caused a customer-facing failure. Half of those that run evaluations had.

Finding 1 — A passing eval is not a working agent

24%

yes, more than once — a recurring gap between evaluation and reality

36%

no such production failure identified

8%

don’t run pre-deployment evaluations; a further 6% don’t track this or can’t say

This is the report’s defining number. Half of organizations (50%) have shipped an AI feature that cleared their internal evaluations and then failed in front of a customer — an incorrect output, a broken workflow, or a quality incident — and a quarter have seen it happen more than once. Only 36% report no such failure, and the remainder either run no pre-deployment evaluations (8%) or don’t track the root cause closely enough to know (6%). The failure is precise and expensive: the evaluation said the agent was ready, and it was not. Everything that follows — how enterprises trust their evals, what they monitor, and how much autonomy they grant — is shaped by this experience.

Finding 2: Almost no one fully trusts automated evaluation

The top complaint: Evals don't match real-world outcomes

We asked which limitation most reduces trust in automated agent evaluations today. Only a sliver of enterprises had no complaint at all.

Finding 2 — Almost no one fully trusts automated evaluation

29%

poor alignment with real-world outcomes — the leading limitation

21%

evaluation bias or inconsistency

18%

lack of explainability

17%

data leakage or privacy concerns

11%

tooling immaturity; just 5% say they fully trust automated evaluation today

Trust in automated evaluation is scarce, and specific. Only 5% of organizations say they fully trust automated evaluation as it stands — meaning 95% name a limitation that holds them back. The most common, at 29%, is the one that most directly explains Finding 1: evaluations align poorly with real-world outcomes, passing agents that later fail. Bias or inconsistency (21%) and a lack of explainability (18%) follow — enterprises cannot always tell why an evaluation reached its verdict — and 17% cite data-leakage or privacy concerns in the evaluation process itself. The tests meant to certify agents are not yet trusted to certify them, which is precisely why the autonomy trajectory in Finding 3 is so striking.

Finding 3: The autonomy ceiling is rising anyway

Two-thirds already allow, or are building toward, zero-human deployment

We asked whether organizations would let an autonomous agent deploy a code or system change to production on automated evaluation results alone, with no human-in-the-loop validation. The trajectory runs straight through the trust gap.

Finding 3 — The autonomy ceiling is rising anyway

34%

yes — already allowed for specific low-risk agents or changes

33%

no — but actively engineering the pipeline to allow it within 12 months

22%

no — and do not expect to permit fully automated deployment for the foreseeable future

8%

not applicable — they don’t deploy autonomous agents; 3% don’t know

Here is the paradox at the heart of the report. Even though almost no one fully trusts automated evaluation (Finding 2), two-thirds of organizations (66%) either already allow zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to permit it within a year (33%). Only 22% rule it out for the foreseeable future. The direction is unambiguous: enterprises are moving to let evaluations gate production autonomously — removing the human check — at the same moment they say those evaluations don’t reliably match reality. The autonomy ceiling is rising faster than the assurance beneath it, which is the mechanism by which the false-confidence failures of Finding 1 will scale rather than shrink.

Notably, the autonomy bet is not just a small company phenomenon. Splitting the sample by company size, larger enterprises are slightly further down the path toward zero human review than smaller companies (70% versus 64%) and slightly more likely to have shipped an evaluation-passing agent that then failed a customer (54% versus 48%). The assumption that large, regulated organizations are holding the human in the loop longest is, in this sample, backwards. To be sure, these are directional figures, since the survey was not a huge sample — 57 respondents from companies with 2,500+ employees and 100 from companies smaller than that.

Finding 4: The evaluation stack is fragmented and provider-led

Provider-native evals lead — tied with no dedicated tool at all

We asked which agent reliability or evaluation platform enterprises primarily use today. The market has no clear leader — and a large share has nothing dedicated.

Finding 4 — The evaluation stack is fragmented and provider-led

17%

use OpenAI Developer Platform native evals/traces — narrowly the most common tool

17%

use no dedicated agent reliability or evaluation tooling at all

13%

Anthropic Claude Console native evals

12%

Confident AI (DeepEval); 11% custom in-house tooling

8%

Braintrust; the pure-play specialists (LangSmith, Weave, Promptfoo, Langfuse, Arize) sit in low single digits

The evaluation layer is early and unconsolidated. Provider-native tooling leads — OpenAI’s native evals and traces (17%) and Anthropic’s Claude Console evals (13%) together outweigh any independent platform — but it is tied at the top by a striking answer: 17% of enterprises use no dedicated agent-evaluation tooling at all, a notable gap for organizations shipping agents to customers. The specialist evaluation vendors — DeepEval (12%), Braintrust (8%), LangSmith, Weave, Promptfoo, Langfuse, Arize — are scattered across single to low double digits, and 11% have built their own. No independent platform has yet become the category standard, which leaves most enterprises evaluating agents with provider-native tools, home-grown scripts, or nothing.

Finding 5: Production monitoring rarely watches output quality

Only a quarter run real-time quality checks on live traffic

Production monitoring for an AI agent can watch two very different things. It can watch whether the system is functioning — is the agent up and responding, did each request complete, how fast, at what cost, with any errors. Or it can watch whether the agent's output is correct — automated checks that evaluate the content of each answer as it goes out: did the agent give the right answer, take the right action, stay within policy. The distinction matters because a confidently wrong answer is invisible to the first kind of monitoring: the request completes, the response is fast, no error is thrown, and every functioning-metric reads healthy. We asked organizations which kind their live production monitoring is built for today.

Finding 5 — Production monitoring rarely watches output quality

25%

transaction trace logging — infrastructure spans, token counts, raw I/O for post-hoc debugging

25%

gateway infrastructure tracking — latency, error rates, and cost at the API layer, not output quality

23%

inline quality assertions — real-time judges/guardrails on live traffic that alert on quality drops

10%

ad-hoc review of live output quality; a further 17% don’t know or say it isn’t their area

Grouped by what is actually being watched, the split is stark: 51% of organizations monitor only whether the agent is functioning, while 23% monitor whether its answers are right. Counting the ad-hoc reviewers and the don't-knows, roughly three-quarters of organizations run no automated, real-time evaluation of output correctness in production — they can see that the system is up and what it costs, and they are taking the correctness of its answers on faith. That blind spot is the runtime counterpart to the pre-deployment gap in Finding 1: the same organizations engineering the human out of the deployment decision mostly cannot see, in real time, when the deployed agent starts getting things wrong.

Finding 6: Bought on cost, measured on consistency

Price and integration drive selection; evaluation consistency is the goal

We asked what most influenced enterprises’ choice of an evaluation vendor, and what they treat as their primary measure of success. Both answers are pragmatic.

Finding 6 — Bought on cost, measured on consistency

28%

chose their vendor primarily on the cost of evaluations — the top selection factor

27%

on ease of integration; 24% on evaluation accuracy

36%

name evaluation consistency as their primary success metric — the single most-cited goal

19%

measure success by speed of experimentation; 18% by reduction in failures or regressions

Enterprises buy evaluation tooling on economics and trust it on repeatability. Cost of evaluations (28%) narrowly leads selection, just ahead of ease of integration (27%) and evaluation accuracy (24%) — breadth of observability (13%) and vendor roadmap (4%) matter far less. On what success looks like, more than a third (36%) name evaluation consistency — getting the same verdict on the same behavior every time — well ahead of speed of experimentation (19%), reduction in failures (18%), production visibility (13%), and compliance (11%). The emphasis on consistency is telling: before enterprises can trust an evaluation’s verdict, they need it to be stable — the very property whose absence (bias and inconsistency) ranked among the top trust limitations in Finding 2. Satisfaction with current tooling is only moderate, averaging 3.8 on a five-point scale across overall satisfaction, ease of implementation, and value for money.

Finding 7: The next dollar goes to humans and observability

Investment is flowing to oversight, not just automation

We asked which reliability and evaluation investment will grow most over the next year. The money is going toward watching agents more closely — including with people.

Finding 7 — The next dollar goes to humans and observability

30%

production observability tooling — the top growth area

26%

human review workflows

20%

safety and policy evaluation

16%

automated evaluation pipelines

8%

say their budget is not increasing

The second-largest planned investment — behind only production observability — is human review workflows, at 26%. Read against Finding 1, that is the report's quietest contradiction: at the same moment two-thirds of enterprises are engineering the human out of the deployment decision, more of them plan to grow spending on human reviewers (26%) than on the automated evaluation pipelines (16%) that would replace them. The zero-human trajectory and the human-review budget are rising in the same companies at the same time. Indeed, only 8% report that their budget is not increasing.

Taken together, enterprises are hedging: building toward autonomy while spending to watch agents more closely and keep humans available for the calls that automated evaluation cannot yet be trusted to make.

Finding 8: A tooling reshuffle is coming

Nearly two-thirds plan to adopt or switch platforms within a year

We asked whether enterprises plan to adopt a new, additional, or replacement evaluation platform, and which they are considering. Few intend to stand pat.

Finding 8 — A tooling reshuffle is coming

36%

have no plans to change

31%

plan to adopt or switch within the next 0–3 months

The evaluation market is wide open. While 36% have no plans to change, a clear majority (64%) intend to adopt a new, additional, or replacement platform within twelve months, and 31% within the next quarter. The consideration set points where current usage is thinnest: Confident AI’s DeepEval leads what enterprises are evaluating (20%), ahead of OpenAI’s native evals (13%) and Braintrust (9%) — the open-source specialists drawing more interest than their present footprint.

Given that so many enterprises today rely on provider-native tools or nothing at all (Finding 4), this is less a defection than a first real wave of tooling adoption — the moment the evaluation layer starts to consolidate. Which platforms earn that trust, in a market where almost no one trusts automated evaluation yet, is the open question this series will keep tracking.

The bottom line: An evaluation gap that autonomy will widen, not close

Organizations with 100 or more employees are granting AI agents more independence than they trust their evaluations to support. Half have already shipped an agent that passed its evals and then failed a customer; almost none fully trust automated evaluation, chiefly because it doesn’t match real-world outcomes; and most watch production for uptime and cost rather than for whether the agent’s answers are right. Yet two-thirds already allow, or are actively building toward, deploying to production on automated evaluation alone.

The vendor market is early and unsettled: the most common primary evaluation tools are provider-native evals, tied with no dedicated tooling at all, and a clear majority plan to adopt or switch platforms within the year. Encouragingly, the next dollar is going to observability and — pointedly — human review, suggesting enterprises sense the gap even as they engineer past it. At 157 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: autonomy is being granted on the strength of evaluations that the people granting it do not yet trust. The evaluation gap is not a coverage problem that more tests alone will close; it is a problem of evaluations that reflect reality and can be trusted to gate it. The open question for later waves is whether assurance catches up to autonomy — or whether the false-confidence failures move from customer incidents into changes that deploy themselves.

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Based on survey responses from 157 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read rather than a precise measurement — the sample is self-selected, not a probability sample, and skews toward the mid-market. Respondents include product and program managers, consultants and advisors, directors of engineering/IT, and CIOs/CTOs/CISOs, among other functions, across technology/software, retail/consumer, healthcare/life sciences, manufacturing, and other industries.