Agent costs will expose the control plane attribution gap

At a glance

What this is: This analysis argues that agent costs will replicate cloud sprawl patterns, with control-plane attribution becoming the critical governance layer for agentic AI.

Why it matters: It matters because IAM, NHI, and AI governance teams need to connect runtime action, delegation, and spend before agent use scales beyond visibility.

By the numbers:

• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
• 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems.
• Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems, meaning organisations failing to scope AI access properly are 4.5x more likely to experience a security incident.
• When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases.

👉 Read SecureAuth's analysis of agent cost sprawl and delegation-chain attribution

Context

Agentic AI costs are a governance problem before they become a finance problem. The article uses cloud FinOps as the analog because the same pattern is already visible: easy adoption, weak attribution, and spend that becomes difficult to explain once activity fans out across tools and sub-agents. In identity terms, the primary issue is not usage alone, but who authorized the action chain and how that chain is recorded.

For IAM and NHI programmes, the missing control is control-plane attribution at the moment of delegation. Cloud billing taught enterprises that reconstruction after the fact is too late; agentic systems can avoid that mistake only if identity, permission scope, and execution context are bound together from the start. That makes the delegation chain a governance object, not just an implementation detail.

This is most relevant where autonomous or semi-autonomous agents already initiate tool calls, create sub-tasks, or trigger downstream actions with cost and privilege implications. The starting position is typical of any fast-moving platform shift: adoption outruns governance, and the bill arrives before the policy does.

Key questions

Q: How should teams govern agent fan-out before costs and privileges spread?

A: Start by treating the planner, sub-agents, and tool calls as one delegated identity chain. Attach business purpose, authorization scope, and audit context at the control plane before execution begins. That makes later spend analysis useful, but more importantly it prevents unchecked action expansion from becoming an ungoverned security and cost problem.

Q: Why do agentic systems create both security risk and cost risk at the same time?

A: Because the same runtime behavior that consumes more tokens also expands authority across tools and downstream actions. When identity, intent, and execution are not bound together, teams lose visibility into who acted, what they were allowed to do, and how much the chain cost. That is a governance failure, not just a budgeting issue.

Q: What breaks when attribution is added after agent activity has already happened?

A: Post hoc attribution can show what was spent, but it cannot prove whether the agent was authorized to act or whether the delegated scope was appropriate at the moment of execution. By then, the action chain is complete, the cost is incurred, and the audit trail is weaker than the control problem it is trying to explain.

Q: Should security teams manage agent spend and agent privilege together?

A: Yes, because separating them encourages blind spots. A high-cost agent chain often signals broad delegation, excessive tool reach, or poor scope boundaries. Managing spend and privilege together gives IAM and platform teams one view of how authority expands at runtime, which is exactly where agentic risk accumulates.

Technical breakdown

Agent fan-out turns one request into a cost chain

In agentic systems, a single human request can expand into a planner, multiple sub-agents, and several tool calls. Each step consumes tokens, and each token-consuming step may also carry identity and authorization implications if it touches systems, data, or infrastructure. That creates a cost chain that is structurally closer to delegated execution than to a single user transaction. The important point is that the chain is not just financial. It is also evidentiary, because every hop can change which identity performed the action and what context survives for audit.

Practical implication: teams need to bind execution, delegation, and attribution together at the control plane before fan-out begins.

Why retrofitted attribution fails in cloud and agentic AI

Cloud FinOps showed that post hoc tagging and billing reconstruction can explain spend, but only after the money is gone. Agents are different in speed, not in principle. If the system that spends the tokens is separated from the system that knows the business purpose, attribution becomes a reconstruction exercise instead of an enforcement control. For identity teams, that means the source of truth for permission and the source of truth for intent must meet at runtime, not in a monthly report.

Practical implication: do not rely on downstream reporting to explain agent activity; capture intent and delegated scope when access is granted.

Delegation chains are the real governance object

The article’s central insight is that the delegation chain is also the cost chain. Once a planner agent spawns sub-agents and tool calls, accountability becomes distributed across identities and actions, which makes simple usage counting insufficient. NHI governance already deals with issued credentials, scoped access, and lifecycle visibility. Agentic AI adds runtime decision-making, which means the chain must be governed as a sequence of identity events rather than a single session record.

Practical implication: model agent activity as a governed identity chain, not a standalone workload log.

NHI Mgmt Group analysis

Agentic AI will recreate the FinOps problem unless attribution is designed into identity from the start. The cloud lesson was not that spend was uncontrollable, but that organisations waited too long to connect usage to accountable identities and business purpose. The same mistake is now visible in agentic systems, where token spend and delegated action can spread across planners, sub-agents, and tools before anyone knows who caused what. The practitioner conclusion is simple: attribution cannot be an after-the-fact report in agentic environments.

The delegation chain is now an identity boundary, not just an execution detail. Once an agent can spawn sub-agents and trigger tools, the relevant governance unit is the full chain of delegated authority. That shifts the problem from “what did the workload do?” to “which identity was allowed to extend action into this next step?” The practitioner conclusion is that access governance must follow the chain, not just the endpoint.

Control-plane visibility is the only practical way to keep agent cost and agent privilege aligned. Billing systems can show token usage after the fact, but they do not establish whether the agent had authority to act, escalate, or recurse. That is an identity governance issue because spend, scope, and accountability are converging at runtime. The practitioner conclusion is that organisations need an operational view of agent authority before they can claim any real control over agent economics.

Ephemeral agent activity changes the economics of governance as much as the mechanics of access. In cloud, unused instances could still be billed for months; in agentic AI, costly actions can accumulate in seconds if the governing layer cannot see delegation in motion. That compresses decision time and makes old review cadences too slow to matter. The practitioner conclusion is that governance must move to the place where the decision happens, not where the invoice lands.

Identity management must now account for runtime decision chains that produce both security exposure and spend exposure. This is where human IAM, NHI governance, and agentic AI converge. A programme that can only answer who logged in is not sufficient when the meaningful question is which identity initiated a chain of actions that consumed tools, credentials, and budget. The practitioner conclusion is that identity programmes need a shared control plane view across all three actor types.

From our research:

• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to The 2026 Infrastructure Identity Survey.
• Another finding from the same survey shows that 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments.
• For teams building governance now, our OWASP Agentic AI Top 10 is a useful next step for mapping runtime risk to control design.

What this signals

Control-plane attribution will become a programme requirement, not a nice-to-have. As agents move from pilots to production workflows, finance and security teams will both need a single answer to who initiated a chain of action, who approved its scope, and what it consumed. That pushes identity governance closer to runtime policy enforcement and away from retrospective reporting.

The practical shift is toward governing delegation as a live security object. Teams that already track workload identity, secrets, and access scope should extend the same discipline to agent fan-out, because cost anomalies often reveal privilege anomalies first. The longer that linkage is missing, the harder it becomes to distinguish efficient automation from uncontrolled autonomy.

With 70% of organisations granting AI systems more access than they would give a human employee performing the exact same job, per The 2026 Infrastructure Identity Survey, the governance gap is already structural. That is why agent economics and agent identity need to be managed together, not in separate operational silos.

For practitioners

• Define agent authority at the control plane Record which agent identities may initiate tool calls, spawn sub-agents, and expand execution scope before any runtime action occurs. Tie that authority to the business purpose so later spend reports are not the first place intent appears.
• Map delegation chains as identity objects Treat every planner, sub-agent, and tool invocation as a linked identity event with scoped permission and audit context. That lets security and platform teams trace cost, privilege, and responsibility through the same chain.
• Separate usage reporting from authorization Use spend dashboards for finance visibility, but rely on runtime policy and audit records for governance decisions. If the control plane cannot explain why an agent acted, the reporting layer is already too late.
• Apply least privilege to agent fan-out Restrict which downstream tools and systems an agent can reach, and narrow that scope further for sub-agents. The goal is to limit both blast radius and token waste when one request expands into many actions.

Key takeaways

• Agentic AI can reproduce cloud-style sprawl, but at a faster pace and with more complicated attribution.
• The main governance gap is not token spend alone, but the inability to connect delegated action to accountable identity at runtime.
• Teams should govern agent fan-out as an identity problem, because control-plane visibility is what keeps cost, privilege, and audit aligned.

Key terms

• Identity Fan-out: Identity fan-out is the expansion of a single request or authentication event into multiple downstream actions, tokens, or service calls. It matters because hidden fan-out can multiply privilege, obscure dependency chains, and make access behaviour harder to audit in production.
• Control-Plane Attribution: The process of binding action, purpose, and accountable identity at the point where execution is authorised. For agentic systems, this is the difference between knowing a task happened and knowing who was allowed to cause it. It is a runtime governance requirement, not a billing export problem.
• Delegation Chain: A delegation chain is the sequence of identities, credentials, and tool calls an agent uses to complete a task across systems. It matters because each step may appear acceptable on its own while the combined path produces an outcome no reviewer would have approved directly.
• Runtime authority: Runtime authority is the permission an AI system has while it is actively deciding and acting, not just when it is approved. In governance terms, it is the point where access, tool use, and action scope become operational, which is why build-time review alone cannot prove safety.

What's in the full article

SecureAuth's full article covers the operational detail this post intentionally leaves for the source:

• The delegation-chain examples that show how planner agents, sub-agents, and tool calls accumulate cost and authority.
• The control-plane framing SecureAuth uses to connect spend attribution with identity governance.
• The practical implications for teams trying to avoid retrofitted attribution after agent activity has already spread.
• The article's broader comparison between cloud FinOps lessons and agentic AI governance.

👉 SecureAuth's full post covers the cloud-to-agent analogy, delegation chains, and the attribution problem in more detail.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.