Insight AI Agent Platform Survey: Only 7% Choose Based on the AI Model

Only 7% of enterprise AI agent orchestration platform users said alignment with a leading AI model was the main reason they chose their platform. Flexibility across models and tools ranked first at 23%, according to a September 2026 survey of 107 platform users.

The findings challenge the idea that organisations automatically choose an agent platform because it comes from the company behind their preferred AI model. Instead, respondents placed greater emphasis on the ability to work across models, maintain production reliability and simplify development.

The same research found that changing models inside a production application often requires substantial engineering work. Among organisations that had switched models, 59% reported either reworking the surrounding application after performance declined or returning to the original model.

We examined the published survey to understand what these findings mean for developers and technical decision-makers choosing an AI agent platform.

Key findings

• 7% prioritised model alignment: Only 8 of 107 orchestration platform users selected native alignment with a leading AI model as their main reason for choosing a platform.

• 23% prioritised flexibility: The ability to work across models and tools was the most common selection factor, named by 25 respondents.

• 59% encountered model-switching problems: Among organisations that changed models in production, this share had to rework their application or revert to the previous model.

• 43% used OpenAI's orchestration platform: It led platform usage among the surveyed orchestration users, ahead of Anthropic at 22% and Google at 21%.

• 63% planned platform changes: This share of all 109 qualified respondents intended to adopt a new, additional or replacement orchestration platform within 12 months.

What an AI agent orchestration platform does

An AI agent orchestration platform coordinates the activities of AI agents. It can decide which tasks an agent performs, which tools it uses and how it interacts with external systems.

For example, a business might use an AI agent to review support requests, retrieve information from internal documents and prepare a response. The orchestration layer manages the steps connecting these activities.

Some orchestration platforms are developed by AI model providers. Others are independent frameworks designed to work with models from several companies.

Provider-native systems can offer close integration with their associated models and services. Independent systems may offer more freedom to change models or combine tools from different providers.

However, flexibility is not automatically equivalent to easier maintenance. Different models can interpret instructions, use tools and produce structured outputs differently.

The survey's model-switching results show why this distinction matters.

AI agent platform selection factors

The September survey asked enterprises using a listed orchestration platform to identify the single factor that most influenced their selection.

Flexibility across models and tools was the leading answer, followed by production reliability and ease of development.

The table covers 107 respondents using a listed platform. Each respondent selected one main factor, and the published percentages are rounded.

These results do not mean enterprises consider model quality unimportant. The question asked for the strongest influence on platform selection, not every feature respondents value.

A company may still prefer a particular model while choosing an orchestration platform for its reliability, tool support or ability to accommodate future changes.

Why flexibility ranked above model alignment

AI development teams often need to connect agents to different applications, databases and internal services.

They may also want to compare models as new capabilities become available or existing models change.

A platform that supports several model providers can give teams more options without requiring them to abandon the entire orchestration system.

However, the survey did not establish that enterprises choosing flexibility actually find model switching easier.

That is an important limitation. Supporting multiple models through an interface does not guarantee that every model behaves consistently.

Applications may depend on model-specific prompts, tool-calling formats, response structures and evaluation criteria.

Developers should therefore distinguish between a platform advertising model compatibility and one that makes model replacement practical in production.

Why switching AI models remains difficult

The survey also asked whether organisations had replaced the underlying model in a production AI application while keeping the surrounding system in place.

Across 109 qualified respondents, 62% reported attempting such a change during the previous year.

Among the 68 respondents who had switched models, 41% said the replacement performed as well or better. Another 38% experienced declining performance and reworked the application, while 21% returned to the original model.

Together, the latter groups account for the reported 59% that required rework or reversal.

Only 19% of organisations that switched described the process as requiring almost no additional engineering effort.

Another 29% spent a few weeks on the change, 12% spent more than a month and 9% stopped before completing it.

These findings suggest that the practical cost of changing models extends beyond replacing a model identifier or API endpoint.

Teams may need to revise prompts, update tool integrations, adjust evaluations and investigate unexpected behaviour.

For developers choosing an orchestration platform, this makes migration testing particularly important.

Which AI agent platforms enterprises use

OpenAI's Agents SDK and Responses API led usage among enterprises running a listed orchestration platform.

In the September survey, 43% reported using OpenAI's platform, compared with 22% for Anthropic's Claude Agent SDK or Managed Agents and 21% for Google's Enterprise Agent Platform.

OpenAI was also the most frequently named primary platform at 25%, followed by Google at 18% and Anthropic at 16%.

These are survey usage figures, not global market shares.

Respondents could select several platforms when reporting usage, so the percentages should not be added together to estimate the share of organisations using orchestration.

Among the 107 platform users, 73% reported using one listed platform, 20% used two and 7% used three or more.

This is relevant because interest in model flexibility does not necessarily mean enterprises currently operate several orchestration systems.

A company can value the option to change models while continuing to use a single platform.

How enterprise preferences changed

The September research also compared selected findings with an August survey.

The share of orchestration users naming model alignment as their top selection factor increased from 2% in August to 7% in September.

The share prioritising performance, including latency and memory, also increased from 1% to 7%.

Both changes were reported as statistically significant under the survey's comparison method.

By contrast, flexibility remained relatively stable, moving from 22% to 23%. That difference was not statistically significant.

These results require careful interpretation because the surveys involved different respondent groups.

The increase in model alignment does not prove that individual organisations changed their priorities. It describes differences between the surveyed samples.

Model alignment also remained less frequently selected than flexibility, reliability or ease of development in September.

Platform adoption plans

The survey suggests that enterprise orchestration choices remain unsettled.

Among all 109 qualified respondents, 63% planned to adopt a new, additional or replacement orchestration platform within 12 months.

This included 28% planning a change within three months, 21% within three to six months and 14% within six to twelve months.

The remaining 37% reported no plans to change.

These are intentions rather than confirmed future purchases or deployments.

Among respondents planning a platform change who named specific options, Anthropic was considered by 33%, OpenAI by 30% and Google by 25%.

The differences between these consideration figures were not statistically significant in the published analysis.

Organisations considering new platforms should account for migration effort, data connections, permission settings and operational monitoring.

Control, security and agent spending

Platform selection involves more than model compatibility.

An orchestration system may control which tools agents can access, which actions they can perform and how their activity is recorded.

Among 94 respondents expecting to deploy autonomous agents at scale, 72% expected to keep at least some orchestration control outside model providers' own platforms by the end of 2027.

This included hybrid approaches, independent orchestration platforms and custom in-house control systems.

However, hybrid governance creates its own problems. Only 22% of respondents expecting a hybrid control plane said they had documented the division of responsibilities by workload.

Spending controls also varied. Among orchestration platform users, 21% either monitored agent spending only after it occurred or had no specific controls in place.

These findings indicate that teams should assess permission management, observability and spending controls before deploying agents with access to important systems.

A platform's ability to connect to several models is useful only when the organisation can also govern what those models and agents do.

Limitations of the survey

The research provides a useful view of enterprise AI adoption, but it should not be treated as a representative measurement of every organisation.

The September survey received 144 responses, of which 109 qualified. The platform selection findings use the narrower group of 107 respondents using a listed orchestration platform.

Respondents came from organisations with at least 100 employees or consultants working directly with such organisations.

The sample included both in-house professionals and consultants, who answered about their main clients.

The research was based on respondents who opted into the survey rather than a random sample of all enterprises.

Some comparisons also involved smaller respondent groups, which limits the precision of the findings.

Finally, the research measured reported preferences and experiences. It did not independently benchmark the reliability, security or migration performance of the orchestration platforms.

We would use these figures to identify practical evaluation questions, not to declare a particular vendor the best choice.

What this means for you

For developers and technical decision-makers, the survey points towards evaluating orchestration platforms as complete operating systems for agents rather than extensions of a preferred AI model.

We recommend the following checks before choosing a platform.

1. Test model portability. Replace the underlying model in a representative workflow and record which prompts, tools and evaluations need changes.

2. Measure reliability. Check whether agents complete tasks consistently and recover appropriately from failures.

3. Review tool integration. Confirm support for the databases, applications and services your agents need.

4. Assess permissions. Ensure agents receive only the access required for their assigned tasks.

5. Check monitoring. Review logs, tool activity, failures and the ability to investigate unexpected behaviour.

6. Set spending limits. Look for automated controls that can stop excessive model usage before charges accumulate.

7. Compare development effort. Evaluate how much code and configuration each platform requires for the same workflow.

8. Document control boundaries. If using a hybrid system, define which platform governs permissions, routing and execution.

9. Test migration paths. Confirm how workflows and stored state can move if the organisation changes providers.

10. Measure total ownership effort. Include maintenance, evaluation, security reviews and model-switching work alongside platform usage.

Teams already committed to a particular model provider may still find its native orchestration platform suitable, especially when integration and administration are straightforward.

Teams expecting frequent model changes should give greater weight to portability and testing.

However, a platform should not be selected on flexibility claims alone. Developers need evidence that its integrations remain reliable when the underlying models change.

Conclusion

The September 2026 enterprise survey found that flexibility across models and tools mattered more to orchestration platform selection than native alignment with a leading AI model.

Only 7% of platform users chose primarily for model alignment, compared with 23% prioritising flexibility.

The findings also expose a practical problem. Among organisations that switched AI models in production, 59% needed application rework or reverted to the previous model.

We recommend selecting an AI agent platform based on tested reliability, model portability, permissions, observability and maintenance requirements. Before committing, run the same production-style workflow across the models and platforms your team expects to use.