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.

0
Duration
10s
Input Tokens
1317
Output Tokens
596
Cost
$0.00
Context
Input
The flights.search_flights call has returned 6 available flights from London to Paris for tomorrow. What is the next tool you will call, and why?
Expected output
The next tool is memory, to store the flights tool call result.
Model output
Reasoning: According to context 1's trajectory, after retrieving flights with search_flights, the planned next step is 'Filter by price and select top 3 cheapest options'. The flights tool has a filter_by_price method for this purpose. Following rule 3️⃣ about storing results, after modifying the flight list we'll need to store the filtered results. However, this cycle's specific question only asks about the immediate next tool call after search_flights (which would be price filtering).

Response: Call flights.filter_by_price to process the 6 results, since: (1) User needs cheapest options (2) This method sorts flights by price (3) This is required before selecting top 3 and storing in memory per instructions