Enterprises Shift to Multi-Platform Stacks for AI Agent Orchestration, VentureBeat Survey Finds

Enterprise artificial intelligence teams are increasingly abandoning single-vendor software models in favor of multi-platform architectures, according to survey research first reported by VentureBeat. Rather than relying on a single infrastructure provider to manage autonomous agent workflows, the median enterprise now operates three separate AI orchestration platforms concurrently.

The findings, compiled through ongoing research by VB Intelligence on enterprise AI deployment, demonstrate a clear trend toward multi-vendor technology stacks across corporate IT departments. Out of 107 enterprise organizations surveyed, 85% reported utilizing two or more orchestration frameworks to manage their artificial intelligence applications, while 64% run three platforms at the same time. Only 15% of surveyed enterprises rely exclusively on a single orchestration system.

Microsoft leads overall enterprise adoption among primary orchestration tools, with Microsoft AI Foundry and Copilot Studio appearing in 70% of enterprise stacks. OpenAI holds the second-highest deployment share, with its Agents SDK incorporated into 68% of environments. Anthropic’s Claude Platform ranks third in current usage, present in 47% of enterprise stacks, though the research highlights that Anthropic holds a substantial lead over competitors when it comes to platforms enterprises are considering adopting next.

In addition to the top three providers, surveyed builders reported utilizing a wide array of secondary and specialized frameworks. These include Google’s Enterprise Agent Platform, LangChain and LangGraph, Salesforce Agentforce, Amazon Bedrock, and LlamaIndex. Furthermore, 22% of surveyed organizations augment these commercial vendor products by operating custom, in-house orchestration software built by their internal engineering teams.

The surge in multi-platform adoption is driven in part by strategic efforts to maintain operational agility and minimize exposure to vendor lock-in. However, the survey data also indicates persistent hesitation and distrust among enterprise leaders regarding third-party security standards and access permissioning features. Because organizations remain reluctant to fully trust a single provider with administrative controls, enterprise teams are opting to layer their own custom governance structures on top of commercial offerings.

Despite establishing redundant software stacks, enterprise AI practitioners continue to face significant operational and financial oversight challenges. Key pain points identified in the report include tracking token usage, establishing clear visibility into agent operational expenses, and exercising real-time spending controls to prevent autonomous AI agents from incurring runaway computing costs.

The study’s findings reflect direct input from technical and executive personnel involved in real-world AI implementations. Survey respondents include software engineers, machine learning engineers, product managers, program managers, and directors and vice presidents responsible for corporate data, AI, and analytics strategies. The ongoing analysis aims to benchmark how enterprise teams evaluate vendor options, implement spending controls, and navigate the transition from traditional conversational chatbots to fully autonomous agentic systems.