Is your AI agent worth what it’s spending?

Enterprise AI adoption has moved fast. Organizations invested in tools, encouraged employees to experiment, and pushed teams to find new ways to work more efficiently. For many, the priority was getting AI into as many hands as possible. 

But now the bills are coming in.

As employees move beyond basic prompts and start building more sophisticated workflows and agents, AI consumption is becoming harder to predict and organizations are starting to ask tougher questions about what they’re getting in return.

Some are responding with tighter usage limits and more scrutiny over AI budgets. The financial discipline makes sense, but broad restrictions can also constrain employees who are using these tools to deliver meaningful value.

Organizations have spent years encouraging employees to adopt AI. Now they need to invest in helping them use it productively and efficiently. That means developing the practical skills to build agents that accomplish real work, operate securely, and consume resources efficiently.

Adoption without proficiency gets expensive

Giving employees access to AI tools is relatively easy; knowing how to use them effectively takes practice.

A chat interface makes experimentation accessible to almost anyone. Building an agent that independently executes tasks, calls external tools, and interacts with enterprise data requires a deeper understanding of how these systems work.

Every configuration decision affects how an agent performs and what it costs to operate.

A powerful reasoning model might be necessary for complex analysis, but excessive for a straightforward summarization task. An agent might have access to dozens of tools when it only needs three. Poorly defined instructions can lead to unnecessary steps, repeated calls, and inconsistent outputs.

These inefficiencies can be difficult to spot when the agent appears to be working. The output looks right, the task gets completed, and nobody has much reason to question what happened along the way. Until the costs start adding up. 

Employees need to understand how their agents operate, recognize where resources are being wasted, and make informed decisions about performance, cost, and risk.

Efficiency and security start with the same discipline

As organizations expand their use of AI agents, spending isn’t their only concern.

A 2026 report from Opsin Labs found that 60% of assessed agents had configured capabilities exceeding their originally stated purpose. The research also found that agent development was expanding beyond engineering into functions such as go-to-market, customer success, and operations, where employees may have less experience managing access controls.

The same attention to configuration that helps control spending can also help reduce unnecessary security exposure. Teams should be able to explain why an agent needs a particular model, which tools it requires, what information it can access, and how much it should reasonably cost to execute a task.

These decisions involve trade offs. A more capable model may justify its higher cost through better results. Additional permissions may be necessary for a legitimate workflow. Effective optimization comes from understanding those requirements and validating the choices made.

This is a skill that improves through hands-on practice.

What optimizing an AI agent actually looks like

Consider an agent designed to produce a daily threat intelligence digest. It gathers information, analyzes relevant developments, and generates a summary for the security team.

The agent works, but there’s room to improve how it operates.

In the demonstration below, we explore four configuration choices that influence its cost, performance, and security.

1. Choose the right model. Different tasks demand different levels of reasoning and computational resources. Moving to a smaller, less expensive model can reduce costs, provided it still delivers the quality the task requires.
2. Review tool access. Agents should only have access to the tools necessary for their intended tasks. Removing unnecessary capabilities reduces complexity and limits potential security exposure.
3. Refine prompts and skills. Clear instructions and reusable skill files help agents execute tasks consistently. They also give teams a way to review, maintain, and improve how an agent works over time.
4. Set execution budgets. Per-run spending limits help prevent inefficient workflows or unexpected activity from consuming excessive resources. Teams can establish budgets based on the complexity and expected value of each task.

Build the skills that make AI sustainable

Enterprise AI spending will face greater scrutiny as adoption grows. Organizations need visibility into consumption, appropriate financial controls, and a clear understanding of the value their investments deliver.

And Employees have a role to play, too. As more people build and deploy agents, they need practical experience making the decisions that affect cost, performance, and security. Model selection, tool permissions, instruction design, and execution limits are becoming essential skills for anyone responsible for agentic workflows.

Those skills develop through experimentation, testing, and refinement. Employees need opportunities to see how their choices affect an agent’s behavior and learn how to improve it to build agents that work, at a cost organizations can sustain.

Learn more about how Immersive can help your workforce build securely with AI here.

‍