As AI agents become capable of taking actions on behalf of users, developers are facing a new challenge: how can those actions be monitored without making every interaction slow and expensive?

OpenAI appears to be exploring one possible answer with its newly announced Decisions API, introduced during its Dev Day event. The technology is designed to make models focus on choosing between a defined set of options, potentially allowing certain decisions to be handled faster and at lower computational cost.

A Different Role for AI Models

Traditional large language models can perform complex reasoning, but using a powerful model for every small decision made by an AI agent can quickly become expensive.

A decision-focused model takes a narrower approach. Instead of asking an AI system to generate a long response, developers can provide a limited collection of possible outcomes and ask the model to determine which option is most appropriate.

OpenAI CEO Sam Altman described the Decisions API as a way for its Luna model to select from predefined choices, including image classifications and different behaviors for AI agents. The approach is intended to retain capabilities such as language and image understanding while making these decisions faster.

The Jev Connection

The announcement has drawn comparisons with Jev, a model developed by TypeSafe AI and introduced earlier in September.

Jev is designed for software automation and works somewhat differently from a conventional chatbot. Developers can give it a set of possible choices, and the model produces probabilities for those options.

That makes this type of model potentially useful as a supporting component alongside larger AI systems. A frontier model could handle complex reasoning, while a smaller decision-oriented model could manage repetitive classification or routing tasks.

The financial difference can become important when these decisions happen thousands or millions of times.

OpenAI’s Decisions API is currently available as a limited preview, so it is still too early to determine exactly how closely its capabilities match Jev or how developers will ultimately use it.

AI Agents Could Be a Major Use Case

One particularly interesting application is AI agent security.

Autonomous agents can browse websites, interact with software, execute commands and make decisions based on their assigned objectives. That creates another layer of risk: an agent may perform an action that technically works but falls outside the task it was supposed to complete.

OpenAI has already been working on additional safeguards for agent behavior, including using separate models to evaluate potentially problematic actions. The challenge is that constantly using a large, expensive model for security monitoring can add substantial computational overhead.

A smaller decision model could potentially act as a low-cost checkpoint.

Turning Every Agent Action Into a Checkpoint

Cybersecurity professional Shapor Naghibzadeh demonstrated this concept through a prototype developed during a recent hackathon. His system used Jev to compare an agent’s intended task with each action it attempted.

The model could allow actions considered safe, block actions it strongly identified as harmful, and send uncertain cases for additional review.

According to the demonstration described by TechCrunch, the estimated cost of monitoring through Jev was dramatically lower than using a frontier LLM for the same type of checking.

That difference matters because an agent may take dozens or hundreds of actions during a single task.

Why This Could Matter for Enterprise AI

The emerging architecture could lead to a more layered approach to AI systems.

Instead of relying on one large model to perform every task, organizations could combine powerful reasoning models with smaller, specialized models responsible for classification, routing, monitoring and security decisions.

The key challenge will be accuracy. A system that is inexpensive but poorly calibrated could introduce new risks of its own.

As AI agents become more autonomous, the ability to monitor their actions efficiently could become just as important as improving their reasoning capabilities. OpenAI’s Decisions API suggests that the next stage of AI development may involve not only making agents smarter, but also building faster and more affordable mechanisms to keep them under control.