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 – fireworks/qwen3-coder-480b-a35b-instruct Task | Opper AI

Input Tokens

38981

Output Tokens

4793

Duration

110804.033

Cost

0

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 user has requested to find information about available flights from London to Paris for tomorrow. The date for tomorrow has already been determined as '2025-05-29' using the calendar tool. The current step is to use the 'flights' tool to search for flights. I need to update the scratch pad to reflect that I am about to call the flights tool, preparing for the next action in the cycle.

Response: Scratch pad updated: Current step: Call the flights tool to find available flights from London to Paris on 2025-05-29.