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

17s

Input Tokens

4409

Output Tokens

286

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. The task is to list all open orders for customer `cust_42`.
   - This requires querying a database for order records tied to a specific customer.
   - The `database` tool’s `list_orders` method is designed for this purpose, as it accepts a `customer_id` parameter.

2. The parameter object must include:
   - `action`: Set to `list_orders` (to specify the operation).
   - `customer_id`: Set to `cust_42` (to filter orders for this customer).

3. No additional tools or parameters are needed at this stage. The response will be handled in subsequent steps (e.g., filtering for 'open' status if necessary, storing results in memory).

4. Compliance with instructions:
   - Rule 1: Schema inspected to confirm `list_orders` requirements.
   - Rule 3: Results will be stored in memory after retrieval.
   - Rule 5: This is a single, atomic action (database query).

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