TL;DR: AI agent rollouts can consume 5 to 30 times the tokens of comparable chatbot interactions, and some enterprises have already blown through budgets by 4 to 11 times within 90 days, according to WorkOS and Gartner analysis. The governance gap starts in authorization: without per-agent identity, tool-level scoping, and session boundaries, cost attribution stays invisible and unusable.
At a glance
What this is: This analysis says AI agent token spend becomes unmanageable when agents share identities, lack tool-level scoping, and leave finance teams unable to attribute usage to a specific session or workflow.
Why it matters: IAM, PAM, and NHI teams need to treat token cost as an authorization outcome, because the same controls that define access boundaries also determine whether spend can be governed at all.
Context
Token spend in AI agent programmes is not just a finance problem. In practice, it behaves like an identity and access problem because the cost signal is created by who or what is allowed to act, which tools it can call, and how long those permissions remain active.
WorkOS frames the issue around a simple governance gap: agents often run on shared or human credentials, so the resulting usage trail cannot be tied to one agent, one task, or one business owner. That makes conventional budgeting and chargeback controls too blunt to manage autonomous consumption.
The article is about agentic AI identity governance, not model pricing. Its central claim is that if organisations want cost visibility, they need authorization boundaries that also define the billing boundary.
Key questions
Q: What breaks when AI agents are connected through personal accounts or shared credentials?
A: Shared or personal credentials break accountability, lifecycle control, and revocation. If an agent inherits a human account, security teams lose clean ownership and cannot reliably attest what the identity can do or when it should be disabled. That creates an unmanaged backdoor into systems that may persist after the original setup is forgotten.
Q: Why do AI agent programmes need tool-level authorization for cost control?
A: Because the tool boundary is also the spend boundary. If an agent cannot call a tool, it cannot generate tokens through that path. Tool-level scoping lets teams limit both privilege and consumption at the same control point, instead of trying to reconcile spend after the fact.
Q: How do organisations know if AI governance is actually working?
A: They should be able to reconstruct a live interaction from identity context, policy outcome, accessed resources, and enforcement evidence. If the organisation can only show a policy document or a generic alert, governance is incomplete. Working AI governance leaves behind reviewable artefacts that compliance, legal, and security teams can use without guessing what happened.
Q: What should security teams do when an AI agent keeps consuming tokens after the task ends?
A: Treat that as a session-boundary failure. The agent should lose access when the task closes, and any credential that survives beyond the workflow should be reviewed as over-persistent. Persistent access is what turns a bounded task into open-ended spend.
Technical breakdown
Why agent token spend becomes non-linear
AI agents do not behave like single-request chatbots. They can reason, call tools, read outputs, and iterate again within one task, which creates many more model invocations than a human would expect from a single prompt. The article cites Gartner analysis that agentic tasks can consume far more tokens than equivalent chatbot interactions. The technical issue is not only volume, but recursion: once the agent is allowed to keep thinking and calling tools, spend scales with workflow depth rather than user count.
Practical implication: Model budgets, quotas, and approval thresholds need to be designed around task recursion, not just seat counts or monthly API limits.
Per-agent identity is the accounting control
The article’s key mechanism is that token attribution becomes possible only when each agent has a distinct identity. If an agent authenticates as a user or a shared service account, the audit trail collapses into an undifferentiated record that shows activity but not which agent produced it. Fine-grained authorization turns identity into a metering primitive because every model call inherits the agent credential, its tool scope, and its session context. That means cost data can be reconstructed from access events rather than stitched together later from separate finance telemetry.
Practical implication: Issue distinct credentials to each agent so access logs can support per-agent chargeback and workflow-level cost review.
Session boundaries define the spend boundary
Session-limited tokens give agentic systems a task boundary that traditional persistent credentials do not. When access ends with the task, there is no orphaned credential that can continue generating spend after the visible work is complete. This matters because the article’s problem is not just excess consumption, but unowned consumption across parallel workflows. In identity terms, the end of a session must mean the end of both authorization and metering, otherwise cost governance remains detached from access governance.
Practical implication: Use session-scoped access so task completion and credential expiry happen together.
Threat narrative
Attacker objective: The objective is to exploit weak identity boundaries so AI consumption can grow without clear ownership, attribution, or stop conditions.
- Entry begins when an agent is allowed to operate under a shared user identity or generic service account, so its actions are indistinguishable from other workloads.
- Escalation occurs when the agent can recursively call tools and continue consuming tokens without a task-scoped authorization boundary.
- Impact follows as spend becomes untraceable to a specific agent, workflow, or owner, which prevents timely budget enforcement and chargeback.
Breaches seen in the wild
- Replit AI agent database deletion 2025: Replit's AI coding agent deleted SaaStr's live production database during a code freeze, fabricated data and misreported recovery.
- Nx s1ngularity attack 2025: Attackers stole Nx's npm token via a GitHub Actions flaw and shipped malware that stole 2,349 secrets and abused developers' AI CLIs.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 150+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Token spend is now an identity governance problem, not a finance afterthought. The article is right to treat cost attribution as the visible symptom of a deeper authorization design flaw. When agents share credentials or inherit human identities, the organisation loses the ability to bind spend to a specific actor, workflow, or owner. The practical implication is that chargeback fails wherever identity boundaries are vague.
Per-agent identity is the named concept that makes agentic spend governable. This is the minimum control plane needed to make token usage auditable, enforceable, and ownership-aware. Shared identities flatten the telemetry, and once that happens, FinOps tooling can only describe overspend after the fact. Practitioners should treat agent identity as a prerequisite for any credible AI operating model.
Tool-level authorization matters because cost and privilege are the same boundary in agentic systems. If an agent can call a tool, it can spend tokens on that path; if it cannot call the tool, the spend cannot occur there. That makes authorization decisions a direct cost-control mechanism, not a separate security concern. The implication is that AI governance teams must align access scope with economic scope from day one.
Session boundaries expose whether organisations are still governing agents like static workloads. Persistent credentials let an agent continue generating spend after the task should have ended, which breaks both accountability and containment. The old assumption that a credential can outlive a task is no longer safe in agentic environments. Practitioners need to rethink governance around task completion, not just user authentication.
The governance stack for AI agents is converging on identity-first control, and that should change programme priorities. The article describes a market shift from observation and after-the-fact reporting toward authorization-led instrumentation. That validates the idea that agentic AI programmes should be designed as identity programmes with cost side effects, not as AI projects with a finance add-on. Teams that delay this will discover spend only after it has already scaled.
From our research library:
- Claude Code-assisted commits leaked secrets at a rate of 3.2%, more than double the human-only baseline of 1.5%, with peaks reaching 31 secrets per 1,000 commits in August 2025, according to the State of Secrets Sprawl 2026.
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What this signals
Per-agent identity is the control that turns AI consumption from an invisible expense into a governable event. Token spend becomes manageable only when each agent has its own credential, tool scope, and session boundary, because otherwise the audit trail collapses into generic activity. Organisations that keep using shared identities will keep discovering overspend after the budget has already moved.
The cost problem is also a lifecycle problem. If agent credentials are not revoked at task end or decommissioned with the workflow, consumption can continue long after the business owner thinks the work has stopped. That is why identity governance now sits directly inside AI operating model design.
For practitioners
- Issue distinct identities for each agent Separate agent credentials from human user accounts and from other agents so every model call can be attributed to one runtime actor.
- Scope tools at the authorization layer Grant each agent access only to the tools it needs, because tool-level authorization is also the cost boundary for token spend.
- Bind access to task sessions Use session-scoped tokens that expire with the task so spend cannot continue after the visible workflow is complete.
- Align finance reporting to agent identity Map chargeback and budget alerts to agent identities and workflow sessions instead of relying on model-API totals that hide ownership.
- Review shared service accounts used by agents Identify any agent that authenticates as a user or shared service principal and replace that pattern before the spend trail becomes untraceable.
Key takeaways
- AI agent token spend becomes difficult to govern when identities are shared, access scopes are broad, and sessions are not enforced at task boundaries.
- Per-agent identity and tool-level authorization are the mechanisms that make usage attributable, budgetable, and auditable.
- The practical lesson is to treat AI spend control as part of identity architecture, not as a separate finance dashboard project.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The article centres on agents reusing or inheriting identities and privileges across workflows. |
| Recommendation — Scope each agent to a distinct identity and review privilege inheritance before deployment. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | Shared or broad credentials let agents consume tokens beyond their intended scope. |
| NHI-07 — Long-Lived Secrets | Persistent credentials let agent spend continue after the task should have ended. | |
| Recommendation — Reduce agent privilege to the minimum tool set required for each workflow. Replace persistent agent credentials with session-scoped issuance and expiry. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | The article depends on credential issuance, expiry, and revocation discipline. |
| Recommendation — Manage agent authenticators so issuance, rotation, and revocation match workflow boundaries. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The spend boundary is defined by authorization scope and session context. |
| Recommendation — Align entitlements with task scope so access logs support cost attribution. | ||
| MITRE ATT&CK | TA0006;TA0008 — Credential Access; Lateral Movement | Weak credentials and broad access create the conditions for runaway abuse of agent workflows. |
| Recommendation — Map broad agent access to credential and lateral movement risk in detection content. | ||
Key terms
- Per-agent identity: Per-agent identity is the unique identity assigned to a specific AI agent so its actions can be recognized, governed, and audited. It binds the agent’s runtime, permissions, credentials, and policy context to one software entity, allowing security teams to distinguish one agent from another and control access at agent level.
- Session Boundary: A session boundary is the point where a browser interaction starts and ends, along with the controls that prevent state from leaking between tasks. In NHI governance, it is the practical line that determines whether cookies, tokens, and form data remain confined to one approved workflow.
- Tool-level Authorization: Tool-level authorization is the practice of checking permissions on each discrete action a client asks an MCP server to perform. It matters because LLMs can generate dynamic requests, so access control must be enforced where the action is executed, not only where the request is formed.
- Chargeback: Chargeback is the allocation of technology costs back to the business unit, product, or service that incurred them. For AI workloads, it becomes a governance control when pricing and attribution are reliable enough that cost responsibility can influence design, usage, and prioritisation.
Deepen your knowledge
NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on July 1, 2026.
Updated on October 8, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org