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

11s

Input Tokens

3604

Output Tokens

239

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: Step 1: Analyze the task - List all open orders for customer ID `cust_42`. This requires querying order data filtered by customer.
Step 2: Review available tools in context. The 'database' tool has 'list_orders' method, which requires 'action': 'list_orders' and 'customer_id' (string). This matches perfectly for listing orders by customer ID. No other tool fits (e.g., email or calendar irrelevant).
Step 3: Confirm schema compliance - Parameters must include 'action' and 'customer_id'. No additional filters mentioned for 'open' status, but assuming 'list_orders' returns relevant orders (likely includes status).
Step 4: Plan atomic action - Invoke 'database' tool with precise params. No prior memory needed; first call.
Step 5: No inspection needed as schema is provided and clear. Follow efficiency: direct tool call.

Response: Tool: `database`
Method: `list_orders`
Parameter object: `{"action": "list_orders", "customer_id": "cust_42"}`