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-pro 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's ultimate goal is to find the top 3 cheapest flights. The previous step was `flights.search_flights`, which successfully found 6 available flights. According to the provided instructions, specifically rule #3, I must 'Always store meaningful results in memory using the memory tool. This means after every tool call, you store the results in memory.'

Therefore, before proceeding to filter these 6 flights by price, my immediate next action must be to store the result of the `flights.search_flights` call into memory. The next logical step after that would be to filter the flights as planned in the scratchpad, but the rule dictates the memory storage must happen first.

Response: The next tool to call is `memory.store_item`. This is because instruction #3 mandates that meaningful results from any tool call must be stored in memory immediately. Since the `flights.search_flights` call has just returned a list of 6 flights, this result must be saved before proceeding with any further actions like filtering.