Opper

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.

Input Tokens

Output Tokens

Duration

Cost

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: 1. The user’s goal is to find the cheapest flights.\n2. We already have the 6 flights returned by `flights.search_flights`.\n3. The next logical step is to sort or filter these flights by price to obtain the cheapest options.\n4. The `flights` tool provides a `filter_by_price` method suitable for this purpose.\n5. According to rule 6, chain tools logically: calendar → flights → filter_by_price.\n6. After calling `filter_by_price`, we will store the top 3 in memory as required by rule 3.\n7. Therefore, the next tool to call is `flights.filter_by_price` and the reason is to obtain the cheapest flight options.

Response: Call the **flights.filter_by_price** tool next. The reason is that we have the 6 flights from London to Paris for tomorrow, and to fulfill the user’s request for the cheapest 3 options we need to filter these flights by price and select the top three cheapest. According to the instructions, we must chain tools logically after retrieving the flights, and the `filter_by_price` method is the appropriate next step before storing the result in memory.