ReAct vs Tool Calling: Why Your LLM Should Decide — But Never Execute

Source: Dev.to

Introduction

When learning LangChain or building AI agents, a common question arises:

If LLMs can decide which tool to use, why do we still execute the tool ourselves in code?

At first glance this seems redundant, but understanding the distinction between decision and execution is critical for production‑grade agentic systems.

ReAct Pattern

The ReAct (Reason + Act) pattern has the LLM produce plain‑text reasoning, for example:

Thought: I need the length of the word "LangChain"

Developer responsibilities in ReAct

1. Parse the text output.
2. Decide whether the suggested action is valid.
3. Execute the tool (e.g., call a function or API).
4. Feed the result back to the model for further reasoning.

Problems with ReAct

• Brittle text parsing – extracting structured intent from free‑form text is error‑prone.
• Hard to validate or audit – actions are not explicitly declared.
• Hallucinated actions – the model may suggest nonexistent tools.
• Scalability issues – unreliable parsing makes large‑scale deployments fragile.

ReAct was powerful for prototyping, but it lacks the safety and structure needed in production.

Tool Calling

Tool calling improves on ReAct by having the LLM return structured intent instead of raw text. A typical response might look like:

{
  "tool": "get_word_length",
  "arguments": {
    "word": "LangChain"
  }
}

The Key Shift

• The LLM decides what to do, but it does not execute the tool.
• Execution is handled by your code (the agent runtime).

This is not a limitation—it’s a deliberate design choice.

Why Execution Stays Outside the LLM

LLMs should not directly execute code or perform irreversible actions because:

• Safety – they cannot guarantee safe execution of arbitrary code.
• Security – direct access to databases, external APIs, or payment systems is risky.
• Compliance – audit trails and regulatory requirements demand clear separation.
• Reliability – you can add retries, timeouts, and sandboxing around tool calls.
• Observability – platforms like LangSmith can trace execution independently of the model.
• Deterministic behavior – the runtime can enforce consistent outcomes.

Correct Execution Model

❌ Wrong model:  LLM decides **and** executes tools
✅ Correct model: LLM decides → system executes → LLM reasons

This pattern is used in:

• LangGraph
• Multi‑agent workflows
• Production AI systems
• Real‑world agent platforms

When to Keep Execution Outside the Model

If you’re building any of the following, maintain the separation:

• AI agents (chatbots, assistants)
• Retrieval‑augmented generation (RAG) pipelines
• Conversational bots with external actions
• Outbound calling agents
• Workflow automation systems

Bottom line: LLMs reason; the surrounding system executes.

Closing Thoughts

ReAct taught us how agents think. Once you internalize the decision‑vs‑execution split, agent architectures become much clearer.

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Discussion prompt: If you’re learning LangChain or building agentic systems, what part confused you the most at first?