How can I enforce that an AI tools agent always checks tools before generating a response?

Below is an approach that combines multiple safeguards rather than relying solely on your system message.

Why Not Just a System Message?

While a system message can instruct the agent to verify tool availability before answering, language models aren’t 100% deterministic. They might sometimes overlook the system instruction, especially if the prompt contains ambiguous or conflicting information.

A Better Approach

1. Enforce an Output Format:
   Use the “Require Specific Output Format” parameter. This option forces the agent’s answer to follow a defined schema where a tool call is explicitly expected if needed. In n8n, this parameter helps by passing a strict parser alongside the prompt so that if a tool should be used, it must be represented in the output correctly.

2. Combine With System Messaging:
   Continue using a system message to instruct your agent—for example, to state clearly that it must verify the available tools via tool_calls before formulating a response. However, this is only one part of the strategy.

3. Iterative Verification:
   Use node options like “Max Iterations” and “Return Intermediate Steps.” These options allow you to review if the agent is making the proper tool calls. If the agent isn’t following the expected workflow, you have the opportunity to catch the lapse before the final output is produced.

Summary

It’s best not to rely only on a system message. By enabling strict output formatting (which enforces a schema that requires tool call information), coupled with iterative checks and a clear system prompt, you create a more robust setup to ensure that the agent verifies the available tools every time.

This combined method leverages the built-in capabilities of the AI Tools Agent and the LangChain tool calling interface to enforce the desired behavior reliably.