Understanding Agentic AI Generative AI

Agentic AI generative AI refers to systems where AI agents make autonomous decisions and generate outputs based on complex prompts. These systems are designed to enhance routing and decision layers, especially in open-source environments.

What is Agentic AI?

Agentic AI involves autonomous agents capable of making decisions and acting within a defined environment. These agents can be programmed or trained to perform specific tasks, including routing, negotiation, or task execution.

Generative AI and Its Role

Generative AI models produce new content or outputs based on input prompts. When integrated with agentic systems, generative AI can facilitate dynamic decision-making, content creation, and complex task handling.

Combining Agentic and Generative AI

The integration of agentic AI with generative models allows for autonomous decision layers that adapt to new data and situations. This combination supports multi-step, multi-turn routing and decision processes.

How It Works in Routing Systems

In routing systems, generative AI can act as a judge or decision-maker, especially when the ensemble is uncertain. For example, a generative judge can be consulted when confidence scores fall below a threshold:

from opensmartroute import LLMJudgeStrategy

router = Router(registry, llm_judge=LLMJudgeStrategy(llm=lambda prompt: my_llm(prompt)), escalate_llm_judge_below=0.6)

This setup enables the system to delegate uncertain decisions to a generative AI judge.

Research Foundations and Economics

Research in economics and mechanism design explores market mechanisms among agents, including strategies like reverse auctions and resource fairness. These principles inform the development of agentic AI systems that operate within economic models to optimize outcomes.

Configuring Generative Routing

OpenSmartRoute provides a flexible framework for generative routing, where an LLM acts as the router or judge. The SDK includes classes like LLMJudgeStrategy for implementing generative decision layers.

Example Configuration

from opensmartroute.strategies.llm_judge import LLMJudgeStrategy

router = Router(
    registry,
    llm_judge=LLMJudgeStrategy(llm=lambda prompt: my_llm(prompt)),
    escalate_llm_judge_below=0.6
)