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.

Agents – gcp/gemini-2.5-flash-lite Task | Opper AI

Input Tokens

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Output Tokens

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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: The user wants the top 3 cheapest flights. The `flights.search_flights` tool has already been called and returned 6 available flights. The next logical step, according to the user's original request and the planned next step in the scratchpad, is to filter these flights by price to find the cheapest ones. The `flights.filter_by_price` method is available for this purpose. Since the user asked for the top 3, after filtering, I will need to select the top 3 cheapest flights and then store them in memory.

Response: The next tool I will call is `flights.filter_by_price`. This is because the `flights.search_flights` call has returned the available flights, and the user's request is to find the *cheapest* flights. The `filter_by_price` method is the appropriate tool to narrow down the results to identify the cheapest options. After this, I will need to select the top 3 and store them in memory.