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

249

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-by-step analysis:
1. Task: List all open orders for customer ID `cust_42`.
2. Review available tools in context: The 'database' tool has a 'list_orders' method specifically designed for this, requiring 'action': 'list_orders' and 'customer_id' (string).
3. Confirm schema match: For action 'list_orders', required params are ['action', 'customer_id']. No other tool (e.g., get_order requires order_id, get_customer retrieves customer data not orders) fits better.
4. No prior memory or data needed; this is the atomic first action per instructions (ANALYZE → PLAN single tool call → ACT).
5. Parameter object: {'action': 'list_orders', 'customer_id': 'cust_42'} precisely matches schema.
6. Post-call: Would store result in memory (e.g., store_item key='cust_42_orders'), but this response identifies the tool and params only.

Response: Choose the 'database' tool with parameter object: {"action": "list_orders", "customer_id": "cust_42"}