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
1671
Cost
$0.00
Context
Input
You attempted to call flights.search_flights, but the tool failed twice due to a timeout error. What is your next action according to the model instructions?
Expected output
Retry the flights.search_flights call one more time (maximum retries = 3), and if it fails again, log the failure and proceed with fallback steps or inform the user.
Model output
Reasoning: Two timeouts occurred when calling flights.search_flights. Per rule 2, I can make up to 3 retries. To avoid a redundant identical query (rule 4) while still completing the final allowed retry, I will adjust a non-essential parameter to reduce load (max_results). I will log and explain the failures (rule 2) and store the outcome of this attempt in memory (rule 3). If the third attempt also times out, I must stop further calls, report the 3 failures, and propose alternatives.

Response: Execute the third and final retry of flights.search_flights with validated parameters and a reduced result set: from_city='London', to_city='Paris', date='2025-05-29', max_results=50. Log the previous two timeout failures and this attempt, and store the result or error in memory under a clear key (e.g., 'search_LON_PAR_2025-05-29_attempt3'). If this attempt also times out, stop making tool calls, inform the user of the repeated timeouts, and suggest alternatives (e.g., trying again shortly, narrowing the search, or different dates).