AI Agent

What Is an AI Agent?

An AI Agent is a rule type that delegates decision logic to a large language model (LLM). Instead of defining explicit conditions and outcomes — as you would in a Decision Table or Decision Tree — you describe what you want the model to do via a natural language prompt and a structured output schema. The model then reasons over the input and returns a typed, structured JSON response every time the rule is called.

What makes AI Agents different from other rule types is their ability to handle judgment, language, and ambiguity. They can read a contract and extract its key terms, evaluate a supplier narrative and produce a risk score, validate a dataset against a policy, or write a plain-language summary of a complex decision, all without a single hard-coded condition.

Each AI Agent is best built around a single, clearly defined task. In practice, the most common patterns are:

• Decision making: evaluating input and producing a decision, score, or routing outcome

• Deep analysis: reading documents, records, and signals to extract structured intelligence

• Report writing: condensing complex input into a concise, decision-ready narrative

• Policy validation: checking data against rules and standards; fixing and normalizing non-conforming records

If your use case doesn't fit any of these patterns, you can define the role and task freely from scratch in the prompt.

When not to use an AI Agent: If your logic can be fully expressed as explicit conditions and outcomes, a Decision Table or Decision Tree will be faster, more predictable, and easier to audit. Use an AI Agent when the complexity or ambiguity of the input makes explicit rules impractical.

AI Agents are always evaluated by solver GAIA (v1), whichever version you call. Learn more

AI Agent vs. Other Rule Types

Parameters	Decision Table	Decision Tree	AI Agent
Input type	Structured values	Structured values	Structured or unstructured
Logic definition	Condition/result rows	Branching conditions	Natural language prompt
Output	Fixed result values	Fixed result values	Typed JSON generated by LLM
Determinism	Fully deterministic	Fully deterministic	Probabilistic (caching available)
Auditability	Full row-level trace	Full branch trace	Explainable AI output (optional)
Best for	Tabular business rules	Hierarchical decisions	Reasoning, scoring, extraction

What You Will Find in This Section

AI Agent DesignerCachingExplainable AI