Why enterprises need an AI control plane for multi‑agent workflows
By Arun Meena, Co-Founder & CEO, RHA OneAI
The conversation about enterprise AI is shifting rapidly from experimentation to operating discipline. Moving from individual agent pilots towards workflows where multiple agents share tasks, tools and information, while maintaining control as the architecture becomes more fragmented, is emerging as a critical technology challenge.
For enterprise technology leaders, including the Chief Information Officer, the Chief Technology Officer and the Head of Information Technology, this makes the control plane an architectural question rather than simply another AI capability.
IBM describes an agent control plane as a system for deploying, operating, monitoring, and governing AI agents across the organisation. IBM recently introduced an agentic control plane within Watsonx Orchestrate, indicating that agent governance is becoming a defined enterprise technology category instead of just an architectural concept.
An individual agent typically receives a task, retrieves defined data and produces an output, whereas a multi-agent workflow distributes these activities across several agents. One agent may research a problem, another might analyse the information, a third might review the result and another may interact with an enterprise application. The value lies in that coordination, but so does a new category of operational risk.
Once agents begin passing work between themselves, traditional application level controls are no longer sufficient on their own. An enterprise must know which agent initiated an action, what authority it had, what information it accessed, which tools it used and whether another agent subsequently acted on its output. Without that context, even a basic routine workflow can be difficult to audit. For example, in an invoice-processing workflow, one agent might review the invoice. Then another agent might check it against procurement rules. And a third agent might submit the invoice for payment. If a mistake occurs, the enterprise needs to know which agent accessed the data, which agent applied the rule and which agent or system authorised the final action.
Policies that are codified or embedded in the agents can be useful but should not be relied on as the sole mechanism for controlling behaviour within an enterprise. It is important that governance controls are independent of the agent itself.
An effective control plane should include capabilities that are familiar to enterprise IT but applied to autonomous agents. Identity and access controls should define which data and systems an agent can access. Rule engines should define what actions an agent may take. Catalogues of tools and agents should provide visibility into the resources approved for use across the enterprise. Monitoring during execution should ensure that agent actions remain within policy. Lifecycle management should ensure that testing, approvals, deployments, and retirement are considered.
Cost governance should also be incorporated into the control plane. By their nature, multi-agent processes can materially increase the costs of model usage due to the number of required model calls and tool invocations, API requests, data retrieval operations, and orchestration steps needed to fulfil the request. Enterprises should have visibility into costs across individual agents, workflows, departments, and business applications. The ability to set limits, allocate budgets, trigger alerts, and require approvals for specific operations can help ensure that organisations can benefit from orchestration without losing financial control. Such functionality is essential for processes involving human oversight, repeated model calls, and large language models and external tools.
The ability to trace is even more important as workflows grow more complex. In an enterprise system, a problem can usually be isolated to a particular set of logs or a specific application. In a multi-agent process, the problem may span multiple models, agents, systems, and applications. This highlights the importance of a control plane focused on runtime execution. It is important for the control plane to recognise that roles, data, models, and even processes are not static. What may be an appropriate level of access and control when an agent is first deployed may not be sufficient over time.
From an IT leadership perspective, it is important that enterprises do not attempt to assign a human to every process or task, as that removes much of the benefit of agentic automation. Rather, enterprises should consider a tiered approach to control, in which rules are applied based on the level of risk associated with a particular task. Activities that have lower risk or are more routine may be subject to greater automation. Activities that involve accessing sensitive data, performing financial transactions, engaging in external communications, or other high-risk activities may require different levels of authorisation and oversight. My experience working with enterprise AI systems has reinforced that a critical aspect of a control plane is the ability to govern the interactions between agents.
Agents are most valuable when they can be integrated into the enterprise’s existing knowledge and workflows. Documents, databases, SaaS applications, APIs, and even internal knowledge must be available to agents through an enterprise data plane that allows them to access the information they need to perform their duties. Creating a control plane is not simply about enabling another AI capability; it is about creating a governed environment where agents can access the data, tools and applications they need while operating within a defined risk profile. This is why the data plane, model plane, and control plane should be considered together as an enterprise begins to develop a system around AI agents.
The decision facing CIOs and CTOs is no longer whether to adopt a particular type of AI agent, but how to enable them to operate within an enterprise environment. These leaders are tasked with working with others across the ecosystem of models and tools, and the system within an organisation will have to effectively govern these interactions without undermining the potential business value.
Enterprises will need to apply the same disciplines they have used to govern other important technology-enabled processes: identity, access, visibility, controls, and accountability. Organisations that excel in these areas will be able to harness the power and flexibility of these agents without sacrificing the oversight and understanding of what happens, why it happens, and when intervention is required.
This is the role of a control plane for AI agents: not to restrict enterprise value creation but to help organisations understand and capture that value at scale. It is not simply another layer of complexity. Rather, it recognises that when software systems act on behalf of an organisation, leaders need clear visibility into how those systems create value and whether they are delivering the expected return.