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
17s
Input Tokens
1280
Output Tokens
692
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 flights.search_flights tool has just produced new data (6 flight options). Rule #3 explicitly states that every meaningful tool result must be stored in memory immediately after the call. We therefore need to persist these 6 flight records before performing any further flight-processing operations (such as filtering by price). Storing them ensures they can be retrieved or referenced in later cycles without repeating the search, satisfying the ‘no redundant tool calls’ rule (#4). Hence, the very next tool invocation should be memory.store_item with a clear key like “flights_raw_2025-05-29”.

Response: memory.store_item – to save the 6 retrieved flight options to memory before any further filtering or sorting.