Agents

AI agent reasoning and tool selection tests planning, tool choice, and self-diagnosis capabilities. These skills are crucial for autonomous ticket triage systems and complex agent workflows where models must decide which tools to use, plan multi-step processes, and diagnose when things go wrong. This represents some of the most challenging tasks as they require open-ended reasoning and decision-making.

100
Duration
21s
Input Tokens
4214
Output Tokens
319
Cost
$0.00
Context
Input
You need to list all open orders for a specific customer whose ID is `cust_42`. Which tool/method do you choose and what parameter object do you send?
Expected output
Use the **database** tool with parameters {"action": "list_orders", "customer_id": "cust_42"}.
Model output
Reasoning: 
    1. **Task Understanding**: The goal is to list all open orders for customer `cust_42`. This is a data retrieval task.
    2. **Tool/Method Selection**:
       - The `database` tool is the correct choice because it handles order-management operations.
       - The `list_orders` method is specifically designed to fetch orders for a given customer.
    3. **Parameter Construction**:
       - The `action` field must be set to `'list_orders'` (per the schema).
       - The `customer_id` field must be populated with `'cust_42'` to target the correct customer.
    4. **Schema Compliance**: The parameters strictly adhere to the schema requirements for `list_orders`:
       ```json
       {
         "action": "list_orders",
         "customer_id": "cust_42"
       }
       ```
    5. **Post-Execution Plan**:
       - Store results in memory under key `open_orders_cust42` (Rule 3).
       - Handle edge cases (e.g., empty results) by logging or escalating if needed.
    

Response: {
  "tool": "database",
  "method": "list_orders",
  "parameters": {
    "action": "list_orders",
    "customer_id": "cust_42"
  }
}