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

3370

Output Tokens

1982

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: High-level plan: (1) Use the database tool's list_orders method for customer_id 'cust_42'. (2) Receive the list of all orders for that customer. (3) Filter locally to open orders and present only those. (4) Persist the final open-orders result to memory if needed for future steps. This provides the required data with a minimal, compliant tool usage.

Response: Tool/method: database.list_orders with parameter object: {"action": "list_orders", "customer_id": "cust_42"}. Rationale: This tool/method retrieves all orders for a specified customer; you can then filter the results to keep only those with status 'open'. This aligns with the provided context where list_orders is defined for the 'database' tool and requires a customer_id.