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

2036

Output Tokens

341

Cost

$0.02

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: The tool flights.search_flights failed twice due to timeout. According to rule #2, max retries = 3, so I can retry one more time. I should also log the failures. Per the instructions, I'll attempt the third retry, and if it fails, log the error and potentially store the failure state in memory before deciding an alternative path.

Response: According to the model instructions (Rule #2: Max retries for a tool call = 3), since flights.search_flights has failed twice due to timeout, my next action is to attempt a third and final retry of flights.search_flights with the same parameters (from_city='London', to_city='Paris', date='2025-05-29'). I will log both prior timeout failures for transparency. If this third attempt also fails, I will: (a) stop retrying (retry limit reached), (b) use the memory tool's store_item to log the failure under a key like 'flights_search_failure' with details of the three timeout errors, and (c) reflect and inform the user that the flight search tool is currently unavailable, rather than making redundant calls.