Boomi just launched something called an Agent Control Tower. It is a centralized management layer for governing AI agents wherever they run. It ships with 1,000 MCP-enabled tools and connects to Claude, Copilot, Gemini, and whatever else your teams are using.
Two days earlier, SAP announced 200 specialized agents orchestrated across finance, supply chain, procurement, and HR at Sapphire 2026. The same week, Xactly shipped a fleet of purpose-built agents for revenue compensation. Three enterprise software vendors. Three agent governance announcements. Same week.
None of them are AI companies.
The middleware layer made a decision
For the last two years, the question in enterprise AI has been: who owns the agents? The answer most teams gave was some version of “we’ll figure it out.” The AI labs built the models. IT bought licenses. Individual teams ran experiments. Nobody owned the orchestration layer.
The integration vendors just filled that vacuum.
Boomi’s CEO Steve Lucas argued that agent success depends on connection, governance, and grounding in trusted data. That is a claim about where the value sits. The company that connects your systems is now positioning itself as the company that governs your agents.
Pay attention to what this means operationally. Boomi already handles data integration for thousands of organizations. SAP runs the financial and HR operations of more than 400,000 companies. When these vendors ship agent governance as a feature, it shows up in the platform your teams already use. There is no separate procurement process. No RFP. No committee decision. The governance layer arrives inside your existing contract.
Why this matters more than model selection
Most enterprise AI conversations still center on model choice. Which foundation model performs best on benchmarks. Whether to go with OpenAI, Anthropic, or Google. Those conversations matter, but they are already losing relevance for the operational question that actually determines outcomes.
The operational question is: when 15 agents are running across your finance, procurement, and support workflows, who decides what they can access? Who sets the boundaries on what data they touch? Who monitors what they produce?
The model vendor provides the intelligence. The governance layer is a different problem. It requires understanding your data architecture, your compliance requirements, your system boundaries. The companies that already map those things are the integration platforms.
Boomi’s Agent Control Tower, built in partnership with AWS through Amazon Bedrock, creates a centralized governance layer that works across models and across cloud environments. Boomi Connect provides secure connectivity between AI tools and enterprise applications through those 1,000 MCP-enabled tools. The integration vendor is deciding which agents can talk to which systems, under what rules, with what access.
The pattern you should recognize
This is the same pattern that played out with cloud migration a decade ago. Managing the transition and ongoing operations was the central challenge. Systems integrators and middleware platforms sitting between the cloud and the business captured value.
The same thing is happening with agents. The model is the capability. The governance layer is the business. And the governance layer is being claimed by the vendors closest to your operational data.
If you run Boomi, SAP, or similar enterprise platforms, this is already happening inside your stack. The agent governance decisions are being made by your vendor’s product roadmap.
What to do about it
Find out what your integration vendors shipped in the last 90 days. Specifically ask about agent governance, MCP support, and orchestration capabilities. If the answer is “we have that now,” your governance layer already has an owner.
Then decide if that is the owner you want. Vendor-provided governance is fast, pre-integrated, and maintained. But it also means your agent boundaries, data access rules, and compliance controls are shaped by your vendor’s defaults, which need review against your team’s requirements.
The part most teams miss: this decision hardens fast. I have watched organizations try to swap governance frameworks after six months of production agent deployment. It is the same pain as switching ERPs mid-operation. Every agent permission, every data access rule, every compliance control has to be rebuilt while the system is live and serving real workflows.
The AI labs built the models. The middleware vendors are deciding where they go. If your AI strategy does not include an opinion on who governs your agents, someone else already has one.