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
24s
Input Tokens
1276
Output Tokens
467
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: I have analyzed the task and context provided. The user has requested the cheapest flights from London to Paris for tomorrow, and the flights.search_flights tool has already returned 6 available flights for the date 2025-05-29. My goal is to filter these results to find the top 3 cheapest options as per the user's request and the planned next step in the scratch pad.

Following the instructions, I am working in cycles (analyze, plan, act, reflect, decide). I have reviewed the available tools and their methods. The 'flights' tool has methods like 'filter_by_price' and 'get_cheapest_option', which are relevant here. Since the user specifically asked for the top 3 cheapest flights, I will use 'filter_by_price' with a parameter to limit the results to 3, ensuring I adhere to rule 1 of getting available methods and parameters before use.

My plan is to call the 'flights.filter_by_price' method next to narrow down the 6 available flights to the top 3 cheapest ones. This avoids redundant tool calls (rule 4) since search_flights has already been executed. After filtering, I will store the results in memory using the 'memory.store_item' method with the key 'top_flights' as outlined in the final step of the scratch pad (rule 3).

I am optimizing for minimal, effective actions (rule 6) by directly moving to price filtering rather than making unnecessary calls. If the filter_by_price call fails, I will retry up to 3 times as per rule 2, logging any issues. After storing in memory, the next step will be to present a summary to the user, completing the remaining goal.

Thus, the next tool to call is 'flights.filter_by_price' because it directly addresses the user's need for the cheapest flights and aligns with the planned next step.

Response: The next tool I will call is 'flights.filter_by_price'. This tool will allow me to filter the 6 available flights from London to Paris for tomorrow (2025-05-29) by price and select the top 3 cheapest options, fulfilling the user's request efficiently.