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 – cerebras/qwen-3-32b 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 next step is to filter the 6 available flights by price to find the cheapest ones. The 'flights.filter_by_price' method allows sorting and limiting results based on price, which directly addresses the user's goal of selecting the top 3 options. Required parameters like 'from_city', 'to_city', and 'date' are reused from the previous search to maintain continuity, and 'max_results' is set to 3 to ensure only the top 3 flights are returned. This action follows the planned trajectory and avoids redundant calls by reusing existing data.

Response: Call 'flights.filter_by_price' with parameters from_city='London', to_city='Paris', date='2025-05-29', and max_results=3 to retrieve the top 3 cheapest flights.