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

16s

Input Tokens

1522

Output Tokens

385

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: Step 1: Analyze the task and context. The user requested the top 3 cheapest flights from London to Paris for tomorrow (2025-05-29). The flights.search_flights tool has been called and returned 6 available flights. The scratch pad indicates the planned next step is to filter by price and select the top 3 cheapest options, then store them in memory.

Step 2: Review available tools. The flights tool has methods including 'filter_by_price', which is suitable for selecting flights based on price criteria from the search results. Other methods like 'get_cheapest_option' might only return one, but we need top 3. 'search_flights' was already used, so avoid redundancy.

Step 3: Plan the next action. Logically, after searching, filter the results for the cheapest options. Assume the 6 flights are stored in memory from the previous call (per rule 3: always store results). Call 'flights.filter_by_price' to get the top 3 cheapest. This chains logically: search → filter → store.

Step 4: Reflect on rules. This is minimal and effective (rule 4). No need for retries yet. After filtering, store the top 3 in memory under 'top_flights' as planned.

Step 5: Decide. Next tool: flights.filter_by_price. Why: To select the top 3 cheapest from the 6 results, fulfilling the user's request for cheapest options.

Response: The next tool I will call is flights.filter_by_price. Why: The search returned 6 flights, but the user wants the top 3 cheapest. This method allows filtering the results by price to identify and retrieve the lowest-cost options efficiently, aligning with the planned step to select the top 3 before storing them in memory.