Single Agent vs Multi-Agent: When to Build a Multi-Agent System

2026-05-04 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

AI agents are applications that use Large Language Models (LLMs) to reason, plan, and utilize tools for task execution, enabling interaction with their environment. Key components include the LLM as the "brain," tools (code functions) for external interaction (e.g., web search, database access), and memory (short-term for current sessions, long-term for cross-session context). The ReAct (Reasoning + Acting) approach differentiates agents from basic chatbots by enabling a core logic loop where the LLM reasons, selects tools, acts, observes results, and iterates until a solution is found. Agents can be single-agent systems for simpler tasks or multi-agent systems, which employ an orchestrator to coordinate specialized agents (e.g., Retriever, Writer, Verifier) for complex, multi-step workflows, as demonstrated by the "Multi-Agent RAG Researcher" project. This project integrates Qdrant for PDF retrieval and Tavily for web search, using SQLite for session memory.

Key takeaway

For AI Engineers designing robust LLM applications, consider adopting a multi-agent architecture for tasks requiring specialized roles, multi-step reasoning, or strong verification. While single agents suffice for simple tasks, complex workflows benefit from an orchestrator coordinating agents like Retriever, Writer, and Verifier, improving modularity and reliability. Evaluate the trade-offs in latency, cost, and maintenance before implementing a multi-agent system, ensuring the task genuinely warrants the increased complexity.

Key insights

AI agents leverage LLMs, tools, and memory, often using the ReAct pattern, to perform complex tasks through reasoning and action.

Principles

• Match agent architecture to task complexity.
• ReAct enhances LLM capabilities beyond basic chatbots.
• Modularize complex tasks with specialized agents.

Method

The ReAct workflow involves an LLM reasoning over a query, calling necessary tools, observing tool outputs, and iteratively refining its response until a grounded answer is produced.

In practice

• Use Qdrant for local document retrieval in RAG.
• Integrate Tavily for up-to-date web search.
• Implement SQLite for session memory in multi-agent systems.

Topics

• AI Agents
• Multi-Agent Systems
• ReAct Approach
• Retrieval-Augmented Generation
• Orchestrator Agent

Code references

• ayoolaolafenwa/multi-agent-rag-researcher
• ayoolaolafenwa/multi-agent-rag-researcher

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.