How to build standard LLM agent systems?
Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents
January 4, 2025
https://arxiv.org/pdf/2501.00881This paper explores "agentic systems," which are AI systems using one or more LLM-powered agents to automate complex tasks. These agents can work together or independently, adapting to dynamic situations.
Key points for LLM-based multi-agent systems include:
- Vertical AI agents: Specialized agents tailored for specific industries, using fine-tuned LLMs and domain-specific knowledge.
- LLM agent architecture: Comprises Memory, Reasoning Engine (LLM), Cognitive Skills (task-specific models), and Tools (for external interaction). The paper introduces Cognitive Skills as a new, key module.
- Agentic system categories: Task-Specific Agents (e.g., ReAct Agent, Router Agent), Multi-Agent Systems (e.g., orchestrated with a lead agent), and Human-Augmented Agents (with human oversight).
- RAG Agent Router: A task-specific agent that routes queries to the appropriate knowledge source within a Retrieval-Augmented Generation system.
- RAG Orchestrated Multi-Agent System: A multi-agent system where a lead agent coordinates the work of specialized retrieval agents connected to different knowledge domains or tools.
- Emphasis on dynamic adaptability and real-time operation: These are key advantages of agentic systems over traditional approaches.
- Focus on practical applications: The paper illustrates the use of agentic systems in areas like customer support, healthcare, legal, finance, and supply chain management.