What is the true cost of an AI agent?

When leaders ask what an AI agent costs, they’re usually shown an invoice for token costs. But building, operating and governing AI agents involves a much broader mix of technology, infrastructure and organizational investment. While some of these costs are clearly visible, the total cost of an AI agent can be difficult to predict, even for experienced technical teams, because many cost categories are hidden, fragmented across vendors and functions, or only become visible after deployment.

As organizations move from AI pilots to enterprise-wide deployment, understanding the total cost of ownership becomes critical to making informed investment decisions and actually driving value from AI.

Seven cost categories make up the true cost of an AI agent

These categories include consumption, platform, infrastructure, governance, workforce, failure and recovery, and compliance. Together, they reflect the full range of costs organizations incur when building, operating and scaling AI agents. Leaders should consider all seven categories when assessing the total cost of deploying AI agents at scale.

1. Consumption costs depend on how agents use tokens and APIs

These are the costs that appear most clearly on an invoice and are often the first ones organizations focus on.

Consumption costs include:

• Volume of input and output tokens – the amount of data processed by the agent during each interaction
• AI model selected – the pricing associated with different models, which varies based on capability and performance
• Complexity of reasoning required – the level of processing needed to complete a task, which affects token usage
• Number of retries, refinements or interactions – the additional cycles required to generate an accurate or final output

AI agents interacting across orchestrated workflows may consume hundreds of thousands of tokens in a single session, compared to the few hundred needed to support traditional generative AI (GenAI) chats.

Key takeaway: Token costs are the most visible costs of agentic AI and may increase as agents become more complex.

2. Platform costs include the subscriptions and licenses needed to run AI agents

Before an AI agent performs any work, organizations need to pay for the foundations on which they operate.

Platform costs include:

• Access to foundation models and AI services – the fees required to use underlying AI capabilities and provider platforms
• Agent orchestration platforms that coordinate workflows – the systems that coordinate how agents interact, execute tasks and manage workflows
• Cloud services – the infrastructure used to host, run and scale AI agents and their supporting systems
• Software licenses – the enterprise tools and applications that agents rely on to complete tasks

Unlike token-based consumption costs, subscription and licence expenses generally remain fixed regardless of how frequently agents are used. However, these costs are fragmented across vendors, making it difficult to gain full visibility of total investment.

Key takeaway: Platform costs are relatively predictable, but their fragmented nature can make them difficult to track and fully account for when estimating total AI investment.

3. Infrastructure costs support how agents run and scale across systems

Running AI agents requires computing power and infrastructure beyond the AI model itself.

Infrastructure costs may include:

• Orchestration runtimes – the software that manages how AI agents coordinate tasks and complete work
• Specialist AI agents – additional agents designed to handle specific tasks as part of a broader workflow
• Business applications – the systems and software that agents access to complete their work
• Cloud infrastructure – the computing resources required to run agents at scale

As agents become more complex and are used across more processes, they rely on more systems and infrastructure to operate effectively.

Infrastructure costs can be easily overlooked because many of these expenses are included on cloud consumption bills, but as agents scale, organizations will need to actively manage these costs rather than simply inherit past technology choices.

Key takeaway: Infrastructure costs often sit within broader technology budgets, making the true cost of AI agents harder to identify.

4. Governance costs help ensure agents operate safely and in compliance

Keeping AI agents secure, auditable and compliant requires investment in governance and oversight.

Governance costs include:

• Guardrails – controls prevent agents from operating outside approved boundaries
• Cyber protections – security measures that protect agents, systems and data from risks
• Attestation processes – mechanisms used to verify and document that agents operate as intended
• Human-in-the-loop oversight – human review and intervention to monitor and guide agent decisions

These costs vary across industry and regulatory environments, and often manifest as different teams operating in isolated pockets across risk and compliance. While these costs compound as agents scale, they can be more predictable when designed in from the outset.

Key takeaway: Governance costs increase as AI agents scale, but are more predictable and manageable when built into solutions from the outset.

5. Workforce costs reflect the people and skills needed to build, manage and oversee agents

AI agents change how work gets done. This means that successful deployment depends on driving organizational change.

Workforce costs include:

• Workforce retraining – upskilling employees to work alongside and manage AI agents
• Role redesign – redefining responsibilities as tasks shift between people and agents
• New operating models – establishing how people and AI collaborate across processes

For some organizations, this may involve labor relations or union negotiations. These investments are often made upfront when an agent enters the workflow but can recur as models and capabilities evolve.

Key takeaway: Workforce costs are critical to realizing value from AI agents, but depend on sustained investment in helping teams adopt new ways of working.

6. Failure and recovery costs arise when agents make errors and require correction or remediation

AI agents can make mistakes, yet many organizations are only beginning to factor in the costs of failure.

Failure and recovery costs include:

• Hallucination remediation – identifying and correcting inaccurate or misleading outputs generated by agents
• Back testing – reviewing an agent’s actions to understand why something went wrong
• Reputational damage – the impact of incorrect or inappropriate outputs on customers or stakeholders
• Lack of governance controls - risks from agents without safeguards such as kill switches to stop unexpected behavior

Some risks can be reduced through safeguards such as human approval for irreversible actions, write-protected environments and predefined boundaries that prevent agents exceeding their authority.

Key takeaway: Failure and recovery costs may be less frequent, but they can be significant if resilience and safeguards are not built in from the outset.

7. Compliance costs address regulatory, legal and reporting requirements associated with AI use

Governments around the world are exploring new approaches to AI regulation. Such changes could affect the long-term economics of AI agents.

Compliance costs include:

• Future reporting obligations – requirements to document and disclose how AI systems are used and governed
• Regulatory compliance requirements – adherence to evolving laws, standards and industry guidelines for AI use
• AI-specific taxes or levies – potential costs introduced through new policy measures targeting AI deployment

Many of these measures remain speculative, but organizations should be aware of them, and their impact on costs as regulation and policy evolve.

Key takeaway: Compliance costs are evolving, but their impact on AI investment is not always fully understood as regulation continues to change.

Looking beyond token costs

The organizations that realize the greatest value from AI will not necessarily be those with the most agents or the biggest AI budgets, but those that understand these costs and make informed investment decisions that balance innovation, governance and long-term business value.

GenAI was used to develop an iteration of this article. In accordance with EY editorial guidelines, the end product was reviewed and edited by EY professionals before publication.