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.

Duration

Input Tokens

4266

Output Tokens

360

Cost

$0.02

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: The task is to list all open orders for customer with ID `cust_42`. Looking at the available tools and their schemas:

1. The `database` tool has a `list_orders` method/action that takes `customer_id` as a required parameter.
2. The schema for `list_orders` action requires: `action` (const: 'list_orders') and `customer_id` (string).

This is the correct tool and method to use. I will construct the parameter object accordingly.

Response: **Tool chosen:** `database`
**Method/Action:** `list_orders`

**Parameter object to send:**
```json
{
  "action": "list_orders",
  "customer_id": "cust_42"
}
```

**Reasoning:**
- The `database` tool exposes a `list_orders` action specifically designed to retrieve orders associated with a given customer.
- According to the params schema, when `action` is `'list_orders'`, two fields are required: `action` (fixed to `'list_orders'`) and `customer_id` (the customer's ID string).
- We pass `"customer_id": "cust_42"` to target the specific customer.
- After a successful call, per memory discipline (Rule 3️⃣), the result should immediately be stored via the `memory` tool with a key like `open_orders_cust_42`.