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
32s
Input Tokens
1280
Output Tokens
2150
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: Analyze: The flights.search_flights step has returned 6 candidate flights for 2025-05-29 (London -> Paris). The user's requirement is to return the top 3 cheapest options. The scratch_pad's planned next step also states: "Filter by price and select top 3 cheapest options."

Plan: Per the agent rules: (1) confirm available methods/params for the flights tool before using it — the context already lists flights methods (search_flights, filter_by_price, sort_by_duration, get_cheapest_option, get_flight_details) and the flights tool params_schema. (2) Choose a single flights tool call that produces the top 3 cheapest results in one step to avoid redundant calls. (3) After obtaining the filtered results, store them in memory with memory.store_item using a clear key. (4) Use up to 3 retries on the tool call if it fails, logging failures.

Act (decision about next tool): The next tool I will call is flights.filter_by_price. Reasoning for this choice: - It directly matches the required operation (filtering by price) and should be able to return the cheapest subset from the six results. - It allows obtaining the top 3 cheapest in a single call (minimizes number of tool calls and adheres to the "avoid redundant tool calls" rule). - Alternatives (e.g., calling get_cheapest_option multiple times) would require multiple calls and be less efficient.

Operational details I will follow before and during the call: - Verify the exact parameters expected by flights.filter_by_price (as required by rule #1) using the flights tool metadata available in context. - Provide the 6-flight list returned by search_flights as input and request a limit of 3 (or the equivalent parameter the tool accepts) and sorting ascending by price. - Set max retries = 3 in case of transient failures, and log any failure if all retries fail. - Immediately after a successful filter_by_price call, store the resulting top-3 list in memory via memory.store_item with a descriptive key such as "top_flights_2025-05-29" and a concise serialized value.

Reflect & next steps: After storing, I will present the 3 cheapest options to the user. If flights.filter_by_price does not accept a limit parameter, I will instead request a sorted-by-price list (or call get_cheapest_option once and then retrieve the next cheapest without repeating unnecessary calls), still minimizing total calls and then store the final top-3 in memory.

Response: I will call flights.filter_by_price next to obtain the top 3 cheapest flights from the 6 search results for 2025-05-29. This single call best matches the required operation (price filtering) and minimizes redundant calls. Before calling, I will verify the flights.filter_by_price parameters (per the rule to inspect tool methods/params), request the top 3 cheapest (or equivalent limit parameter), allow up to 3 retries on failure, and then store the resulting top-3 in memory under a clear key (e.g., "top_flights_2025-05-29").