Agentic AI has moved from conference hype to a budget line item. This is where the conversation gets more interesting and more uncomfortable. Unlike traditional AI systems that respond to a single prompt, classify a document, recommend an action, or generate a summary, agentic AI systems are designed to pursue goals. They plan, call tools, inspect results, retry failed steps, consult memory, hand off tasks to other agents, and sometimes critique their own work before producing an answer or taking an action.

That extra autonomy is the value proposition. It also introduces the cost problem.

A single chatbot interaction may consume a few thousand tokens. A useful agentic workflow can consume hundreds of thousands or millions of tokens per day because it does more than answer a question. It decomposes the problem, retrieves context, reasons through options, invokes APIs, checks the output, and often runs multiple passes before reaching a result. Therefore, the economics need to be understood at the level of “agent instances,” not just model calls.

For the estimates below, I am using a blended token cost of $3 dollars per million tokens. This is not intended to reflect a single vendor’s list price. It is a blended planning figure that assumes a mix of input and output tokens, reasoning steps, retrieval-augmented generation, summarization, tool calls, memory updates, and occasional use of larger context windows. Some enterprises will pay less through volume discounts or by routing work to smaller models. Others will pay more by using premium models, long-context prompts, web browsing, large document ingestion, and repeated reasoning loops.

The basic formula is straightforward. If an agent consumes 2 million tokens per day, it consumes 730 million tokens per year. At $3 per million tokens, that single agent costs about $2,190 per year in token burn. That number sounds surprisingly low until you multiply it by the number of agents, workflows, and users, plus the surrounding infrastructure required to run these systems safely.

What an agent really costs

In the model used here, the annual token-only cost per agent ranges from about $1,095 to $3,833, depending on the use case.

• A lightweight HR recruiting or onboarding agent that consumes one million tokens per day costs about $1,095 per year.
• A more demanding software engineering agent that consumes 3.5 million tokens per day costs about $3,833 per year.
• Customer support agents are roughly $2,190 per year.
• Legal contract agents cost about $2,409 per year.
• Security triage agents run about $2,738 per year.
• Research agents are around $3,066 per year.

These figures are useful but incomplete. They include only LLM token consumption and exclude orchestration platforms, vector databases, observability, model evaluation, security controls, workflow monitoring, human review, enterprise application integration, data pipelines, audit logging, prompt management, and the engineers needed to build and maintain the systems. In real deployments, I would expect the all-in operating cost to be two to five times the raw token cost. For regulated or mission-critical environments, the multiplier can be even higher.

This is where many agentic AI business cases become less clear. The model call may be inexpensive, but the system around the model is not. An agent that can update a customer relationship management system, approve a refund, generate a purchase order, or recommend a security containment action needs guardrails, permissions, logging, rollback mechanisms, and human escalation paths. These are not optional features. They are the difference between a demo and an enterprise system.

Use cases that make economic sense

Customer support is one of the more obvious use cases. A typical support automation deployment may use eight agents: an intake classifier, a knowledge retrieval agent, a response drafting agent, an escalation agent, a quality review agent, a CRM update agent, a sentiment detection agent, and an analytics agent. At two million tokens per agent per day, each agent costs about $2,190 per year in token burn, bringing the annual total to roughly $17,520. If that system deflects even a modest number of tickets or improves agent productivity, the economics can be attractive.

Sales development is another practical example. A five-agent system for account research, lead enrichment, email personalization, CRM updates, and follow-up scheduling may consume 1.2 million tokens per agent per day. That results in an annual cost of about $1,314 per agent, or $6,570 for the full agent team. This can be compelling if it improves pipeline quality, but it can also be wasteful if agents generate low-quality outreach at scale. The cost of brand damage is not measured in tokens.

Software engineering is more expensive but potentially more valuable. A 12-agent system covering requirements analysis, architecture, code generation, testing, review, security checks, documentation, CI debugging, refactoring, release notes, dependency analysis, and hot-fix support may consume 3.5 million tokens per agent per day. That works out to about $3,833 per agent annually or roughly $45,990 for the full system. Compared with engineering salaries, the token cost is small. The real question is whether the system reliably improves throughput without increasing defects, security vulnerabilities, or maintenance complexity.

Security operations also fit the agentic model because the work is repetitive, time-sensitive, and context-intensive. A 10-agent security triage system could include agents for alert triage, log analysis, threat intelligence, endpoint investigation, network investigation, incident summarization, ticketing, compliance evidence, escalation, and post-mortem. At 2.5 million tokens per agent per day, the annual token cost is about $2,738 per agent or $27,375 for the system. This is easy to justify if it reduces alert fatigue and accelerates response, but risky if the agents hallucinate causality or bury critical signals in confident summaries.

Finance, legal, healthcare administration, market research, HR, and supply chain are also viable:

• A six-agent finance close system may cost about $9,855 per year in token burn.
• A four-agent legal contract review system may cost about $9,636.
• A seven-agent healthcare administrative workflow may cost about $13,797.
• Competitive intelligence using six agents may cost $18,396.
• HR recruiting and onboarding using five agents may cost $5,475.
• Supply chain planning and exception management using eight agents may cost $21,024.

Across all 10 example use cases I’ve mentioned in this section, the model assumes 71 agents and a total annual token burn of about $175,638.

The comparison with traditional AI

The economics of agentic AI should always be compared with simpler approaches. Traditional AI, workflow automation, rule engines, robotic process automation, and non-agentic LLM calls are often cheaper, easier to govern, and more predictable. Agentic AI is usually overkill for tasks like classification, extraction, summarization, routing, or drafting within a narrow context. A deterministic workflow with a single model call can do the job at a fraction of the cost and risk.

Agentic systems make sense when the process requires judgment across multiple steps, dynamic planning, tool use, exception handling, and adaptation to incomplete information. They are valuable when the path to the answer cannot be fully scripted in advance. They are less valuable when enterprises use them as a trendy substitute for basic automation.

The best architecture is usually hybrid. Use traditional automation where the process is stable. Use non-agentic AI where the task is bounded. Use agentic AI only where autonomy creates measurable leverage. That means fewer agents, tighter scopes, explicit budgets, model routing, token monitoring, and human checkpoints for high-impact decisions.

The financial mistake many organizations will make is to treat agents as digital employees with near-zero marginal cost. They are not. They are probabilistic software components that consume tokens, trigger tools, create operational dependencies, and require supervision. The token bill may be manageable. The governance bill may not be.

Agentic AI can absolutely be worth the money. In many cases, the annual token burn for a useful agent team is less than the loaded cost of a single employee. But that is not the same as saying it is cheap. Companies must measure agent cost per completed business outcome, not cost per prompt or cost per model call. In the end, the question is not how much an agent costs. Ask yourself the right question: Does the autonomy an agent provides outweigh the complexity it introduces?

AI governance is an ongoing game of catch-up for enterprises. Model updates and iterations are rolling out at a rapid clip, often making governance frameworks obsolete before they’re battle-tested.

To evolve beyond this paradigm, OpenAI is introducing Active sessions. This new ChatGPT security feature allows users to review and log out of one or more sessions through a simple interface. The feature is now available across all ChatGPT accounts and workspace types, including personal and managed workspaces. Experts call it an important development for the model provider, which currently has 1 billion monthly active users.

Previously, organizations often had limited visibility into where users were logged in, and simply relied on password resets or broad account actions to force re-authentication, noted Ensar Seker, CISO at SOCRadar. “Granular session control is a more efficient and less disruptive approach. From a governance perspective, session transparency improves accountability and supports investigations,” he explained.

A holistic view across session activity

Active sessions allows admins to see known browser and app sessions across ChatGPT, Codex, and API Platform. Specifically, they are able to view device and browser information, approximate location, sign-in date and time, whether a device is trusted, and whether the session is current.

To access the feature, users can go to ‘Settings’ > ‘Security’ > ‘Active sessions.’ They can then log out of specific sessions and remove devices from trusted services. They also have the ability to log out of all sessions (thus ending sessions across devices), however, this action can take up to 30 minutes to complete.

However, OpenAI emphasizes that session details may be “approximate or incomplete,” and that the feature has limits. It does not show or manage connected apps or third-party app sessions, sign-ins through third-party services, Codex CLI sessions, or recently signed-out sessions.

Further, Active sessions cannot be used with accounts linked to an enterprise’s single sign-on (SSO), including security assertion markup language (SAML) and OpenID Connect (OIDC).

Better late than never

While Active sessions is an important security and governance development, experts note that the feature is basic, and was a long time coming.

“The reality of OpenAI offering the ability to end active sessions on ChatGPT by administrators is that it’s something that exists in lots of platforms,” said David Shipley of Beauceron Security. “They should’ve had it sooner, but better late than never.”

From a security standpoint, he noted, OpenAI could do a better job policing ChatGPT to prevent it being used by threat actors to host malware, which is the latest threat to enterprises.

SOCRadar’s Seker also pointed out that this type of visibility and oversight is something that enterprises have expected from SaaS platforms for years. “It allows administrators and users to quickly identify unauthorized access, terminate stale sessions, and reduce the risk of account compromise persisting undetected.”

Iterative upgrades disrupt governance

Last week, OpenAI updated GPT-5.5 Instant in both the ChatGPT app and API to “improve response style and quality,” the company said. It had rolled out GPT-5.5 Instant earlier in May as a successor to GPT‑5.3 Instant, calling it “generally smarter” and prone to fewer hallucinations.

According to OpenAI, the update makes GPT-5.5 Instant “easier to read, more natural in everyday conversations, and better paced in practical help tasks, with fewer overly long or bullet-heavy responses.”

But even with tools like Active sessions, enterprises continue to struggle with governance amidst seemingly continuous iterative model updates. It’s simply not sustainable, said Beauceron’s Shipley: “How do you build an appropriate testing plan with a nondeterministic system?”

SOCRadar’s Seker pointed out that many organizations perform security, compliance, and business validation testing before approving a model for production use. But, “when model behavior changes under the same version family, previously documented assumptions may no longer fully reflect actual performance,” he noted.

“The biggest governance challenge in AI is not model adoption, it’s model change,” Seker said. “Most organizations can evaluate a model once. Far fewer are prepared to continuously evaluate how that model evolves over time.”

This particularly creates challenges for regulated industries where auditability, repeatability, and change management are critical, he said. Even beneficial improvements can introduce governance concerns if organizations are not clearly informed about what changed and when.

Valence Howden, advisory fellow at Info-Tech Research Group, noted that organizations often can’t assess the implications of model iterations against their boundaries, and, worse, are often unaware of them.

While the biggest enterprise challenge was initially tied to which AI model was being used, what that model did, and who owned it, iterative updates can muddy those waters and increase reliance on third party practices and tools that organizations often don’t have the resources for, he noted. 

“Without the ability to opt out [of an update] before it’s incorporated, [enterprises] are basically red-teaming the updates with their clients,” said Howden.

The ongoing game

Security teams today are pushed to their limits because they are expected to manage rapidly evolving models, new features, and changing behaviors, while maintaining compliance, risk management, and business continuity, said SOCRadar’s Seker.

“Governance is difficult because organizations are no longer evaluating a static product,” he said. Rather, they are managing a “continuously evolving service” where capabilities, integrations, and user behaviors can change far faster than can traditional security review cycles.

Info-Tech’s Howden agreed, saying that enterprises’ existing governance practices, especially accountability, are poor, as are their risk practices.

“It’s hard to suddenly become good at things they’re already poor at doing,” he said. “They are also incentivized for speed and innovation, so they ignore governance as a constraint, or don’t want to do it at all.”

How enterprises should respond

Seker advised that, ultimately, organizations should treat AI models as living systems rather than fixed software releases.

Security and governance programs should include continuous validation, monitoring, and periodic re-assessment instead of sole reliance on one-time approval processes, he said. Enterprises should also establish clear vendor change management expectations, including requiring transparency around model updates, behavioral changes, and potential impacts to existing workflows.

“Effective AI governance increasingly depends on visibility into change, not just visibility into risk,” Seker said.

Cloud computing has reached a crossroads. The high cost and data sensitivity of AI workloads are raising the appeal of private clouds, even as neoclouds and sovereign clouds shake up the cloud provider landscape. New cyberthreats, shifting compute requirements, and management complexity are adding to cloud complications.

Download the June 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World, and learn how to navigate the latest cloud strategy developments.