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 – openai/gpt-5-nano 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: Plan to narrow the current 6 results to the 3 cheapest using the flights tool's filter_by_price, then persist the top options in memory before reporting back. This aligns with the user's goal and the defined workflow.

Response: Next tool call: flights.filter_by_price with max_results=3 to extract the three cheapest flights from the six already returned for tomorrow. Rationale: the user wants the cheapest options and the top 3; filtering by price and limiting results yields the desired set. After obtaining them, we will store the results in memory under the key 'top_flights' and then present a summary to the user.