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

Output Tokens

Duration

Cost

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: Analyzing the provided context and instructions, I noticed that after calling the flight tool with the parameters for flights from London to Paris on 2025-05-29, the next required action is to update the scratch pad accordingly. The updated scratch pad should record that the flight tool has been invoked to retrieve available flights, mark the progress of the task (step 2 of 4 completed, flight tool invocation in progress), and set up the next action (to interpret and compile the flight search results).

Response: Scratch pad updated: 'Current step: Flight tool called to get available flights from London to Paris for 2025-05-29. Awaiting flight search results. Next steps include interpreting the results and compiling them for the user.'