According to a new study by VentureBeat Pulse Research conducted in June 2026, there is a clear organizational trend toward consolidating around model-provider platforms for the management and orchestration of AI agents, with Anthropic and its Claude model leading by a wide margin. This choice is primarily driven by "model gravity," and success is measured by the ability to reliably execute multi-step tasks. However, the study reveals a significant gap between enterprise ambitions and reality on the ground: most "agents" currently deployed in organizations are actually simple single-prompt chatbot wrappers, the control systems enterprises plan are deliberately hybrid to prevent vendor lock-in, and real-time financial control over token burn remains the exception to the rule.
Research Methodology and Respondent Characteristics
The VentureBeat survey was fielded as part of its ongoing Pulse Research series, with this specific instrument focusing on enterprise agent orchestration. Respondents were filtered to organizations with 100 or more employees (sample size n=101), collected during a single wave in June 2026. Because this is a single measurement wave rather than a pooled multi-month sample, the report presents a cross-sectional snapshot and does not infer month-over-month trends.
In terms of organization size, the sample is evenly distributed across the enterprise bands: 21% of respondents come from organizations with 100–499 employees, 21% from organizations with 2,500–9,999 employees, and 21% from organizations with 50,000 or more employees. The bands of 10,000–49,999 employees and 500–2,499 employees represent 19% of the sample each. By role, the respondents are senior and highly credible for purchasing decisions: product and program managers (15%), Chief Information, Technology, and Information Security Officers (CIO/CTO/CISO - 13%), consultants and advisors (13%), and a mix of data, AI, and engineering directors and VPs, with other functions accounting for 18%. Regarding purchasing power, 81% of respondents identify as recommenders, influencers, or final decision-makers for AI solutions (66% recommenders/influencers, 15% final decision-makers). The Technology/Software sector is the largest represented industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%). The publishers note that with 101 respondents, the sample is robust enough to present general directions with reasonable confidence, although it remains a self-selected group and is not a probability sample.
Finding 1: Orchestration Runs on Model-Provider Platforms
When enterprises were asked which agent orchestration platform they primarily use today, the answers concentrated on the major model providers, and on one in particular. It should be noted that these figures represent the leading platform within each enterprise among the active AI technical decision-makers who participated in the survey, and do not represent a measure of market share by total financial spend.
Model platforms clearly dominate the field. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101 organizations), while open-source libraries (such as LangChain or LangGraph) and custom in-house developments, which often anchor developer-level discussions, see only single-digit usage rates. Anthropic’s lead—with 40% of organizations choosing it as their primary platform, more than double the next closest platform—reflects the logic of "model gravity": organizations select the orchestration layer that comes packaged with the base model they wish to use. Only a small 3% of organizations reported that they are not orchestrating at all.
Respondents rate the platforms they run at an average of 3.94 out of 5 overall (based on 109 responses), with "value for money" specifically scored at 3.94, and "ease of implementation" receiving the weakest score at 3.85. These scores place orchestration near the bottom of VentureBeat’s five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, combined with the fact that 96% of users plan to change their orchestration approach within the next year, indicates only a provisional acceptance of current tools: the platforms work well enough to run today, but not well enough to stop the search for improved alternatives.
Finding 2: Model Gravity Drives Platform Selection
The single most influential factor in choosing an orchestration platform is the pull of the underlying base model (Model Gravity)—that is, native alignment with the state-of-the-art model the organization has standardized on (21% of respondents). However, the next tier of factors points to complexity: flexibility across different models and tools (17%) and ease of development (17%) show that organizations want to avoid being trapped by a specific choice, foreshadowing the lock-in concerns detailed later. Security and permissions (14%) and total cost of ownership (TCO - 11%) round out the pragmatic considerations. Performance (such as latency and memory) sits last at only 4%, serving as a reminder that at this stage of adoption, the binding constraints are model fit and optionality, not raw operational speed.
Finding 3: The Job is Reliable Multi-Step Execution
Enterprises' primary success metric for orchestration systems focuses on reliability and managing complex workflows. Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101). In the view of enterprises, orchestration succeeds when it is capable of reliably guiding a task through multiple steps to full completion. Developer productivity (17%) is important but remains secondary, and end-user experience (9%) is a minor concern, indicating that orchestration is perceived as an internal execution challenge rather than a user interface design issue. This reliability-first standard makes the "chatbot trap" finding even more striking: enterprises define success as dependable multi-step execution, yet most of their deployed "agents" do not perform multi-step work at all.
Splitting the sample by organization size reveals that the trap is not evenly distributed: 77% of smaller enterprises (under 2,500 employees) report that a quarter or less of their agents perform true multi-step work, compared to 62% of larger organizations. Larger enterprises are further along in deploying genuine multi-step processes, while the chatbot trap is directionally a mid-market condition.
Finding 4: Consolidate, Productionize, and Build In-House
When asked what major changes they anticipate in their orchestration strategy over the next 12 months, three primary actions clustered at the top with nearly identical shares: building a custom in-house control plane (25%), standardizing on a single framework (24%), and moving agents from sandbox environments to active production environments (23%). These figures show that organizations are transitioning from experimentation to operational consolidation: they want fewer frameworks, more production exposure, and greater ownership over their control layer. Only 4% of organizations expect no change in their strategy. The desire for custom in-house control planes stands out alongside the high concentration on model-provider platforms—enterprises are aligning with provider platforms while simultaneously planning to wrap them in their own control logic.
Finding 5: Investment Flows to Workflow Tooling
In response to which orchestration-related investment will grow the most next year, agent workflow tooling leads the list at 34%. Following closely are security and permissions enforcement (25%) and scaling infrastructure (20%)—the investments required to transition agents from sandbox environments to production. Monitoring and debugging draw a smaller share at 11%, with another 11% reporting flat budgets. The weight given to tooling, permissions, and scaling over pure observability signals that enterprises are spending money to build and harden orchestration, not merely to watch it run in real time.
Finding 6: The Control Plane Will Be Hybrid – And Lock-In Is Why
A clear majority of organizations (51%) expect their primary agent control plane to reside in a hybrid model (combining native provider tools with external orchestration) by the end of 2026. Only 6% of organizations expect to hand control entirely over to a provider-managed service. Collectively, architectures that keep control (at least partially) outside of the model provider—including hybrid models, custom builds, and externally abstracted systems—account for 88% of the sample (89 of 101 organizations). The reason for this architecture surfaces directly from worries about provider-resident control: vendor lock-in leads the risks at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across different models and tools (21%). This finding demonstrates that while enterprises are willing to build on a provider's platform, they refuse to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they fear most.
This June 2026 figure marks a shift from earlier periods. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a higher share (12%) expected to hand control fully over to a provider-managed service. Additionally, vendor lock-in has become a more prominent concern: in the April–May wave, the leading concern was security and permissioning limitations (32%), while lock-in was ranked second at 24%. By June, these two concerns traded places, indicating that enterprise worries are maturing from initial security questions to the future ability to switch providers.
Finding 7: The Chatbot Trap – Most "Agents" Aren't Agents Yet
When asked to assess their portfolios honestly, organizations revealed a deep gap: 71% of enterprises (72 of 101) report that a quarter or less of their deployed "agents" are true, multi-step orchestrated workflows, and only 10% (10 of 101) have crossed the halfway mark (where more than 50% of their agents fit this description). Technological ambitions and complex control architectures are being built long before the actual portfolios are ready for them. The current reality consists overwhelmingly of simple, single-prompt digital assistants dressed as "agents." The publishers note that budgeting systems, platforms, and strategies are being established precisely because the orchestrated portfolio is still so thin on the ground.
Finding 8: Fiscal Control Is Still Reactive
Finally, the study examined the enforcement of financial controls over agent token consumption—the risk that an autonomous loop will exhaust a budget before anyone can intervene. Most organizations rely on native platform caps or after-the-fact monitoring, while real-time programmatic control remains the exception.
More than a quarter of organizations (27%) admit they have no real-time, programmatic way to stop an agent that has gone out of control before the monthly bill arrives—they discover it from the logs afterward. Another 32% lean entirely on native caps and throttles built into their primary platform, a control that is only as good as the provider’s tooling and ties back to the lock-in concerns of Finding 6. Only those enterprises building custom gateways (23%) or exploiting cross-model routing to balance costs (19%) treat token consumption as an engineering problem to be controlled deterministically. Here, too, a split appears by company size: roughly 34% of enterprises with fewer than 2,500 employees exercise only reactive control over agent spend, compared to 20% of larger organizations.
Summary and Outlook
In summary, organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly but maturing slowly. They are standardizing on model-provider platforms (led by Anthropic's Claude at 40%), choosing platforms based on the underlying base model, and measuring success by the reliable execution of multi-step processes. Investments are directed toward workflow tooling and permissions, and the goal is to consolidate frameworks and move agents to production while maintaining a hybrid control plane out of fear of vendor lock-in.
However, honest self-assessments puncture these high ambitions: 71% report that a quarter or less of their agents are truly orchestrated, and less than a quarter are capable of stopping a runaway agent in real time. Organizations have decided how they want to manage agents well before most of their agents are performing actions that require such management. The open question for the future is whether operational reality will catch up with ambition, or whether the chatbot trap will prove more persistent and stubborn than enterprise roadmaps assume.