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.

100
Duration
30s
Input Tokens
1082
Output Tokens
342
Cost
$0.00
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: First, I review the current context and scratch pad. The user requested flights from London to Paris for tomorrow, and we've already used the calendar tool to determine that tomorrow is 2025-05-29. The current scratch pad indicates we're at the step of interpreting that result and deciding the next action. The remaining goals include calling the flights tool and then compiling results.

Based on the instructions, I need to update the scratch pad since I've decided to call the flights tool. This involves setting the current step to calling the tool with the correct parameters (from_city: 'London', to_city: 'Paris', date: '2025-05-29'), adjusting the remaining goals by removing or marking the completed one, adding any relevant notes, and updating the progress from Step 2 of 4 to Step 3 of 4, assuming the steps are getting the date, calling the flights tool, interpreting results, and presenting to the user.

This keeps the agent on track in the cycle-based operation, ensuring logical progression toward the user's goal without unnecessary loops.

Response: Current step: Call the 'flights' tool with parameters: from_city: "London", to_city: "Paris", date: "2025-05-29".
Remaining goals:
- Interpret the results from the 'flights' tool.
- Compile and present the flight information to the user.
Notes:
- Confirm that the date format matches tool requirements (YYYY-MM-DD).
- Store flight search result in working memory for summary.
Progress: Step 3 of 4.