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 – openai/gpt-5-nano Task | Opper AI

Input Tokens

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Output Tokens

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Duration

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Context

Input

You have decided to call the flight tool, update the scratch pad

Expected output

The scratch pad is updated with the new action, example model_output: Current step: Called 'flights' tool with parameters (from_city: London, to_city: Paris, date: 2025-05-29); waiting for tool result.
Remaining goals:
- Receive and process the flight search result.
- Summarize available flight options and present them to the user.
Notes:
- Ensure the result includes key details (flight numbers, times, prices if available).
- Store the raw flight result in working memory for reference.
- Prepare final summary once results are in.
Progress: Step 3 of 4.

Model output

Reasoning: The next required action is to query available flights for the identified route and date, based on the previously determined date from the calendar tool. Updating the scratch pad ensures traceability and prepares the memory store for summarizing results once the tool returns.

Response: Initiating flight search using the flights tool with from_city='London', to_city='Paris', date='2025-05-29'. Scratch pad updated to reflect this action and to store results for a summary after retrieval. Progress: Step 3 of 4.