Modern AI architectures are transitioning from static chatbots to autonomous agents. These agents achieve goals by executing tools (e.g., searching databases, calling APIs, or running system commands). This behavior is powered by **Function Calling**. However, if your system instructions are poorly structured, agents will hallucinate arguments, trigger the wrong tools, or get stuck in infinite loops. Here is how to write prompt boundaries for agentic systems.
1. The "Think-Action-Observe" Loop (ReAct Framework)
To make an agent reliable, you must force it to log its thinking process before it attempts to call a function. This is known as the Reason-Act-Observe pattern. structure your prompt like this:
For every turn, you MUST follow this sequence:
1. Thought: Analyze the user's goal and decide which tool is needed.
2. Action: Call the tool using the exact JSON format specified.
3. Observe: Read the tool output and explain the result to the user.By forcing the model to generate a "Thought" token first, you allow the neural network to compute the logic before committing to a function argument.
2. Restricting Tool Output Formats
LLMs are notoriously prone to sending incomplete JSONs. In your system prompt, define a strict schema validator and write explicit formatting constraints:
When executing a tool call, output ONLY a valid JSON block enclosed in markdown code fences. Do not output conversational text before or after the JSON.
Example:
```json
{
"name": "send_email",
"arguments": {
"to": "user@example.com",
"subject": "Hello",
"body": "Your order is ready."
}
}
```3. Graceful Error Handling
If a tool fails (e.g., a database connection timeout), the agent must not crash. Teach the agent how to handle errors within the prompt: "If a tool returns an error code, explain the error in your next Thought step, adjust your parameters, and try an alternative tool or report the limitation to the user."