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.

Duration

Input Tokens

1319

Output Tokens

932

Cost

$0.00

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 logical action is to use the 'filter_by_price' method from the flights tool to sort the 6 available flights by cost in ascending order and return the top 3 cheapest options. This aligns with the user's explicit request to prioritize cost-effectiveness. After filtering, the results must be stored in memory using the 'store_item' method to conserve data for the final summary presentation. The process follows the tool chaining rule (calendar → flights → memory) and avoids redundant calls by leveraging existing flight data from the previous step.

Response: Call flights.filter_by_price with max_results=3 to get the cheapest 3 flights from the 6 available options, then store the results in memory using memory.store_item(key='top_flights').