AI agents vs RAG: how ReAct works and why multi-agent systems are needed

LLMs can generate text, but for most practical tasks, a single answer is not enough. Action is needed: request data from an external source, select the appropriate tool, verify the result — and if necessary, adjust the next step. This is exactly how AI agents work, and this is precisely why the agent-based approach is rapidly becoming the standard for systems built on large language models.

Agent vs Simple LLM

A classical language model is a question and answer. One request, one generation, stop. An agent is a cycle: the model reasons, selects an action, receives a result from the external environment, and repeats the process until the task is solved.

The key difference from RAG systems (Retrieval-Augmented Generation): RAG adds context from a knowledge base before generation — this is passive enrichment. An agent decides on its own when and what to request: it calls APIs, runs code, reads files, accesses external services. It doesn't just receive a hint — it acts and adapts to what the environment returns.

Another fundamental difference: an agent can change its plan during execution. If a search returns an unexpected result, the agent reformulates the query or switches to a different tool. RAG cannot do this.

How ReAct Works

ReAct (Reasoning + Acting) is one of the foundational and most studied frameworks for agents. The model sequentially goes through three phases in a cycle:

• Thought — reasoning: the model formulates what is already known and what needs to be done next
• Action — tool selection and invocation: web search, calculator access, API requests, or database queries
• Observation — analysis of the returned result and transition to the next iteration

The cycle repeats until a final answer is obtained. ReAct works well on short and medium reasoning chains — 3–7 steps. On longer tasks, errors accumulate, so it is often combined with additional mechanisms: verification of intermediate results, step count limits, explicit output formatting. ReAct's strength is in transparency. Every step can be checked and debugged: you can see what the model "thought," what it called, and what it received in return.

Multi-Agent Systems

A single agent is limited: by context window, specialization, execution time. When a task is complex or requires parallel work, multi-agent systems come into play — an architecture where multiple agents work together. A typical multi-agent system structure:

• Orchestrator — a controlling agent that decomposes the task and distributes subtasks among workers
• Workers — specialized agents for specific functions: search, code generation, data processing, communications
• Critic / Verifier — a validation agent that checks the results of other agents before final assembly

This architecture allows independent subtasks to be executed in parallel and significantly reduces the risk of error accumulation, which in a single chain can grow from step to step.

"The agent-based approach is rapidly becoming the standard for modern LLM-based systems" — from the "Basic

Essentials" series.

Practical Example: Agent in Google Colab

At the conclusion of the "Basic Essentials" series, a minimal working agent is demonstrated — a travel planning assistant implemented in Google Colab. Everything is reproducible: no hidden dependencies, minimal configuration. The agent can search for destination information through external tools, create a route based on user preferences, and clarify details in dialogue if the request is ambiguous. This example clearly shows how a working agent fundamentally differs from simply calling an LLM with a long prompt: it doesn't guess — it requests, receives, and adapts.

What This Means

AI agents have ceased to be an academic concept. Understanding basic patterns — ReAct, orchestrator/worker separation, multi-agent architectures — is becoming necessary for everyone building products on LLMs. Without this foundation, it is difficult to predict where a system will break, and almost impossible to debug it.