Deterministic AI

Picture this: you have a task that has to be done every day. It doesn’t change from one day to the next, and the factors surrounding the task are usually the same. 

This is a situation where deterministic AI shines. In this article we’ll explain what deterministic AI is, what it excels at and struggles with, and how you can harness deterministic AI in Port to expand the capabilities of your AI agents.

What is deterministic AI?

Deterministic AI refers to systems where the same input will always produce consistent outputs. A deterministic model is designed with consistency in mind; unlike probabilistic models, which incorporate randomness (or, as the name implies, probability), deterministic models follow predefined rules to return predictable results. 

As an example, a rule-based system that determines if a service is healthy based on a fixed threshold, such as CPU usage being under 70 percent, is a deterministic system. Whenever CPU usage is below 70 percent, the system will come to the conclusion that the service is healthy. 

This consistency is what makes deterministic AI agents most effective in situations where predictability, accuracy, transparency, and trust are vital.

Pros and cons of using deterministic AI

How to use deterministic AI effectively

Knowing the limitations of deterministic AI can help you put its strengths to use, particularly when building AI agents. You want agents that are predictable, transparent, accurate, and not able to take destructive actions; deterministic AI can ensure agents meet all of those standards.

When creating AI agents in Port, you can build deterministic context models to handle consistent, repeatable tasks with self-service actions, such as: 

• Setting up an agent to handle load balancing to ensure your consumer base has steady service during high-traffic periods.
• Using AI agents to set up time-restricted production environments for your developers, so your human team can work more efficiently without risk of driving up resource costs.
• Automating observability maintenance to monitor workload health and address issues if any drift is detected.

Port’s AI agents offer consistent, controlled behavior: you know what permissions the agents have, what the agents can access, and what tools they can run. Because of this, the limited adaptability of deterministic AI can work in your favor when used thoughtfully in Port. This, in turn, will change your developers’ role, eliminate the toil of repetitive work that affects developer experience, and shift what work your human engineers focus their time and resources on.

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FAQs

What is deterministic AI?

Deterministic AI refers to systems where the same input will always produce consistent outputs. Unlike probabilistic models, deterministic systems follow predefined rules to return predictable results. Because of this consistency, AI agents based on deterministic models are effective in situations where predictability, accuracy, transparency, and trust are vital.

Where is deterministic AI typically used?

Deterministic AI is typically used in places where it can handle consistent, repeatable tasks. With Port, you can set up AI agents that reliably reproduce work related to tasks like:

• Monitoring safety-critical systems like load balancers, production environments, and SSO/RBAC.
• Reading and analyzing expert systems and decision trees.
• Triaging issues in fraud detection systems with rule-based logic.

What are the advantages of deterministic AI?

Deterministic AI is effective when handling tasks that don’t require adapting to new information. Because of this, it can predictably provide reliable, transparent, and accurate results, while also being relatively easy to test and verify.

What are the limitations of deterministic AI?

Because deterministic AI depends on pre-defined knowledge, models are frequently inflexible. They have limited adaptability in dynamic environments and can require extensive rule setup or manual programming before they perform effectively.

Is AI probabilistic or deterministic?

AI can be either probabilistic or deterministic. The categorization is based on how the foundational model is set up and how the AI agents are used.

How is deterministic AI different from probabilistic AI?

Deterministic AI is predictable, rule-based, and is most effective when used for consistent, repeatable outcomes. Probabilistic AI involves randomness or uncertainty, so it’s a more practical choice in situations where the same input may yield different outputs.

Can deterministic AI be combined with probabilistic AI?

Yes, hybrid approaches to deterministic and probabilistic AI are used to cover a variety of needs. Deterministic logic for safety and compliance can be combined with probabilistic machine learning for adaptability and pattern recognition. Real-world examples include: 

• Self-driving cars using deterministic safety checks (“if obstacle distance < safe braking distance, stop the car”) on top of probabilistic perception models (determining what is a pedestrian, cyclist, car, etc.).
• Consumer recommendation systems (think Netflix or Amazon) using probabilistic machine learning to show relevant items per-user while also using deterministic logic to enforce rules like, “always include at least 20% new releases,” or, “do not recommend out-of-stock items.”
• Cloud security and access control systems using probabilistic machine learning to detect unknown threats (e.g. gauging the likelihood that a login or API call is malicious), then using deterministic rulesets to enforce guardrails and compliance requirements (e.g. “block root logins from non-corporate IP ranges”).