Opper

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.

Agents – cerebras/llama-4-maverick-17b-128e-instruct Task | Opper AI

Input Tokens

44310

Output Tokens

4566

Duration

63166.849

Cost

0

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 task is to determine the next action after the 'flights.search_flights' tool failed twice due to a timeout error. According to the context, the user needs the cheapest flights from London to Paris for tomorrow (2025-05-29). The previous action was to call 'flights.search_flights', which failed twice. The instructions state that the max retries for a tool call is 3. Therefore, the next step is to retry 'flights.search_flights' one more time. If it fails again, we need to log and explain the failure.

Response: Retry 'flights.search_flights' one more time with the same parameters (from_city='London', to_city='Paris', date='2025-05-29'). If it fails again, log the failure and consider alternative actions or notify the user.