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.

Input Tokens

54100

Output Tokens

7695

Duration

247813.792

Cost

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: Let me analyze the situation step by step:
1. The flights.search_flights tool has failed twice due to timeout errors
2. According to the instructions, we have a maximum of 3 retries for tool calls
3. We've used 2 retries so far, so we have 1 attempt remaining
4. The failure pattern suggests a potential system-wide issue or high latency
5. Checking the instructions, we need to:
   - Log and explain failures
   - Work in cycles (analyze, plan, act, reflect)
   - Store meaningful results in memory
6. Since we have one retry left, it's important to:
   - Document the previous failures
   - Make one final attempt
   - Have a fallback plan if the third attempt fails

Response: The next action should be:
1. Store the timeout errors in memory using memory.store_item with a key like 'flight_search_errors' to log the failures
2. Make one final (3rd) attempt with flights.search_flights
3. If the third attempt fails:
   - Store the final failure in memory
   - Report back to the user about the technical difficulties
   - Suggest alternatives (like trying again later or using a different date)