For most of the last decade, the data workflow ended at an answer. A business leader asked a question, an analyst turned it into SQL, an engine ran it, and a dashboard surfaced a number. The person reading that number still had to interpret it, decide what it meant, and go do something about it. The dashboard formalized that limitation rather than fixing it, and the real work, the distance from knowing to doing, fell to humans after the fact. That is where weeks and sometimes months still vanish today, which is part of why traditional BI is failing the modern enterprise.
AI removes the analyst from the middle of that path, and on the surface it looks like pure progress. It is faster and cheaper, with no ticket queue and no three-day wait. But the step that disappeared was doing more work than what it looked like on paper. The analyst was not a bottleneck so much as a filter, the person who knew which table to use, which date range applied, and how revenue was calculated for this corner of the business.
That is the real reason enterprises now need an agentic control plane. The goal of enterprise AI is no longer analytics that merely inform, but analytics that act. And action, unlike a passive recommendation, has to be governed.
Why an agent needs a control plane
Consider revenue. Finance means recognized revenue, the audited number on the income statement. Sales means booked ARR, the annualized value of signed contracts. Product means gross billings, the raw amount invoiced before discounts and refunds. Each definition is correct from the perspective of the team that uses it, and each is incomplete without the context that travels with the question.
A human analyst knows which one to apply, or corrects course the next time. An agent gets a query, picks one definition on its own, and runs it across thousands of questions before anyone notices the answer is wrong.
Importantly, this is not a model problem. Every enterprise is running on roughly the same foundational models, and those models reason correctly. They are simply reasoning against the wrong definition or making a confident guess. Swapping one model for another does not solve it, because the gap is access to business context, not raw capability.
So the danger is not that agents are incapable. It is that they are fast, confident, and wrong, at a scale no human reviewer can catch after the fact. A control plane exists to prevent exactly that. It is the governance boundary where you coordinate agents, tools, and complex AI workflows, enforce policy on AI-driven actions, and set the guardrails that keep flexibility from turning into corporate risk.
Where the control plane sits
Starburst understands the enterprise intelligence platform to consist of four layers, with the control plane as the third layer. Underneath it is a federated analytics engine built on Trino that queries data in place rather than forcing migrations. On top of that sits the enterprise context layer, which applies common definitions, metadata, and policies so raw data becomes a shared ground truth that humans and agents can both read. The agentic control plane is the layer where that grounded context turns into safe action, and the agentic interface above it preserves model optionality.
The order matters. You cannot govern actions on data that has no agreed meaning. Agent grounding comes first, and the control plane is what enforces it across every workflow.
Why an agentic control plane matters right now
Two forces make this urgent. At the top, AI and application volatility are constant. The models, parameters, and tools your team picks today will not be the ones they rely on next year. At the bottom, enterprise data gravity is real. This means that data is heavy, fragmented, regulated, and expensive to move. Forcing heavy data to migrate at the frantic speed of volatile models creates risk, overhead, and instability. A control plane absorbs the volatility above while respecting the gravity below, so you are not rebuilding governance every time a model changes.
Importantly, the cost of getting this wrong is no longer theoretical. An overwhelming share of companies now report that AI spending is eroding their gross margins, most are missing their initial cost forecasts entirely, and a majority are planning to repatriate AI workloads just to regain predictability. Running agents on a fragmented estate with no governing layer is how those numbers happen.
Trust is the other reason. Organizations only hand real work to a system once it has proven trustworthy, and they do it progressively. First, the insights have to be accurate and available on demand, with clear reasoning and cited evidence. Then the system has to recommend actions that fit how the business operates, not just actions that are technically logical. Only then can routine workflows be delegated to automation with clear guardrails, where humans engage on exceptions. That is all made possible via the control plane.
How to build an agentic control plane
An agentic control plane requires one thing above all: context. A control plane is only as trustworthy as the semantic layer beneath it. Definitions, business rules, and the questions a data product is designed to answer have to travel with the data, not sit in a wiki page or someone’s head. Certify them, give each one an owner, and let nothing reach an agent until a human has signed it off. When a definition changes, it should revert to draft until it is re-certified.
Think in terms of tools
Coordinate tools, not just queries. Real workflows reach outside your own estate. A client layer for external services lets an agent call an SEC filings database, an email system, or a ticketing tool, while the control plane decides what it is allowed to touch and prompts a human for permission when it crosses a boundary. The agent combines external information with internal data through a single interface, rather than scattering the work across separate applications.
Codifying repeatable workflows as skills
One important point is the need to codify repeatable work as skills. Ad hoc exploration is a useful accelerator, but production needs consistency. Skills let you author an institutional workflow once so that every run is identical, regardless of who triggers it. AIDA Skills are how you encode a counterparty review or a contract impact analysis as a vetted, repeatable process rather than a fresh guess each time.
Humans in the loop keep you grounded
Keep a human in the loop until trust is earned. Design the workflow so the agent generates the report, and a person reviews it before anything is sent to a risk officer or a customer. Once the same workflow has proven itself repeatedly, you can let it send automatically. The control plane should make that a configuration choice, not a rewrite.
Auditability is the pathway to governance
Make everything auditable. Every agent should log a decision trace, and every answer should be traceable back to the definition it used and the person who certified that definition. Embed security, access control, and lineage directly into the data foundation rather than bolting them on afterward. That is what lets you safely expose a distributed data estate to AI and keep full compliance while staying operationally flexible. Guardrails against prompt injection, topic restrictions, and per-user data access belong in this same layer.
A functional agentic control plane is essential for agentic AI success
Done well, a control plane changes what AI is for. Instead of a chat agent that answers and leaves the work to you, you get a system that proposes the next step, takes it under clear guardrails, and leaves an audit trail behind it. That is the move from analytics that inform to analytics that act, made safe enough to put into production.
The investment in AI beyond coding and a handful of other categories is still in its early stages, meaning most industries have not yet decided what trustworthy AI looks like to them. The organizations that climb the trust ladder deliberately and expediently, rather than stumbling up it by accident, are the ones that will set the pace and define what AI looks like in their industry. A control plane is how you make this function in production.
