Boomi, the data activation company for AI, announced major platform innovations designed to solve critical barriers to enterprise AI adoption. At the core of these updates is Boomi’s Agent Control Plane, AI-native infrastructure that securely connects AI agents to core business systems, provides governance over agent activity, and controls runaway AI costs. This critical infrastructure runs flexibly across public cloud, the customer’s own cloud (VPC), or on-premises, directly supporting data and digital sovereignty, and giving organizations greater operational control over their AI estate.
Key Takeaways
- Boomi’s Agent Control Plane provides the critical AI-native infrastructure that operationalizes agentic workloads with full control. Vendor and model-neutral, it connects AI agents to core enterprise systems, grounds execution in verified business data with full data lineage, and governs the actions they take.
- According to Gartner®, “By 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance gaps identified only after production incidents occur.” Boomi’s Agent Control Plane addresses the governance gap that stalls enterprise AI by centralizing visibility for agents and tools through an AI gateway enforcement layer, curbing token cost overruns and mandating human-in-the-loop approvals.
- Boomi’s Agent Control Plane delivers maximum flexibility and choice by securely governing agents, tools, and models across the ecosystem, including bring-your-own-models (BYOM) and specialized SLMs. This governance operates within private networks or regional boundaries to reinforce data sovereignty, protect sensitive IP behind corporate firewalls, and optimize compute costs.
- The Boomi Enterprise Platform drives measurable enterprise AI ROI by converting natural language intent directly into multi-system workflows, while opening the platform to builders through expanded APIs and agent skills, programmatic orchestration, and agent trust scoring to scale operations across the business.
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The AI Governance Gap Creates a New Generation of Enterprise Risk
AI adoption is rapidly outpacing governance. Enterprises are moving agents into production faster than they can see what those agents are doing, what they cost, or what happens when one touches the wrong system. That gap introduces severe operational, financial, and security risks: unpredictable token spend, loss of proprietary intellectual property, compliance exposure, and no single view of what AI is doing inside the business. Faced with that uncertainty, many organizations fall back on the only lever they have: shutting access down. Restricting connectivity keeps the enterprise safe on paper, but it starves agents of the systems and data that make them useful, stalling AI’s value.
Organizations should not have to choose between moving fast on AI and keeping control of their data and infrastructure. AI agents need deep access to core transactional systems such as Salesforce, SAP, Oracle, and Workday to deliver real business value, but granting that access without dedicated policy enforcement and strict boundary controls exposes critical corporate intellectual property to public models and unauthorized data leaks. This balance of control is the largest unmanaged risk in enterprise technology today. That lack of governance is precisely why so many AI initiatives stall in the pilot stage, and why agents already in production run without security auditability or clear financial accountability.
Independent research quantifies both halves of the problem:
- Governance. Gartner predicts that “by 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance gaps identified only after production incidents occur.” Gartner also states that “Applying uniform governance to all AI agents, regardless of their autonomy level and scope, can lead to enterprise AI agent failure.” (Gartner, May 2026).*(Gartner, May 2026).
- Cost. In the FinOps Foundation’s 2026 State of FinOps survey of 1,192 practitioners representing more than $83 billion in annual cloud spend, 98% said they now manage AI spend, up from 31% two years earlier, making AI cost management the most in-demand skill in the discipline (FinOps Foundation, February 2026).
- Trust. In “The Agentic AI Readiness Gap,” a Forrester Consulting thought leadership paper commissioned by Boomi in July 2026, 86% of leaders said their organizations had moved beyond AI agent pilots, but only 34% said they trust the actions their agentic systems take. Organizations that deployed agents before they were ready reported an average of $2.1 million in added cost.
“The governance conversation has shifted from model risk to execution risk. Enterprises are no longer primarily worried about what an agent says. They are worried about what it does to a general ledger or a customer record, what that action cost and whether anyone can reconstruct the decision six months later in an audit. Cloud and model providers each govern their own estate, which leaves the average enterprise holding several partial audit trails and no complete one. The organizations getting this right are putting enforcement in the path between the agent and the system of record, rather than alongside it,” said Michael Barnes, Chief Analyst, Enterprise IT, Omdia.
SOURCE: Businesswire