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.

100
Duration
24s
Input Tokens
1280
Output Tokens
1583
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: We just completed flights.search_flights and received 6 results for London → Paris on 2025-05-29. Per rule 3, we must store results in memory after every tool call before proceeding. I’ve verified the memory tool’s available method (store_item) and its parameters (key, value). Therefore, the next tool call should be memory.store_item to persist the 6-flight payload (e.g., key: "flights:LON-PAR:2025-05-29:search_results", value: the JSON of the 6 results). After persisting, we can safely proceed to select the top 3 cheapest options without re-querying, complying with the no-redundant-calls rule.

Response: memory.store_item — to persist the 6 returned flights (key: "flights:LON-PAR:2025-05-29:search_results") so we comply with the "store after every tool call" rule and can then filter/select the top 3 cheapest without re-calling search.