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: The next logical step is to fulfill the remaining goal: use the flights tool to search for flights from London to Paris on 2025-05-29 (tomorrow, per the calendar tool). I should record this decision in the scratch pad so that the agent’s state and future cycles clearly reflect the chosen action and its parameters.

Response: Updated scratch_pad:
Current step: Call the flights tool with parameters {from_city: "London", to_city: "Paris", date: "2025-05-29"}.
Remaining goals:
- Await flights tool result and store it in working memory.
- Compile and present the flight options to the user.
Notes:
- Ensure that any returned flight list is captured exactly as provided for later summarisation.
Progress: Step 3 of 4.