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

3616

Output Tokens

190

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: The task requires listing all open orders for customer ID 'cust_42'. Reviewing the available tools in the context, the 'database' tool has a 'list_orders' method specifically designed for this purpose. Its parameter schema requires 'action' set to 'list_orders' and 'customer_id' as the customer ID. This matches the need exactly, and no other tool is relevant for retrieving order data. Per instructions, I analyze the context (order management via database), plan the atomic action (call list_orders), and will act by invoking this tool with precise parameters. After success, I would store the result in memory, but the task asks only for the chosen tool/method and parameter object.

Response: Chosen tool: database
Chosen method: list_orders
Parameter object: {"action": "list_orders", "customer_id": "cust_42"}