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.

Duration

46s

Input Tokens

1327

Output Tokens

385

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: Step-by-step thought process:
1. Review context: User wants flights from London to Paris for tomorrow. Calendar tool returned '2025-05-29', which is in correct YYYY-MM-DD format for the flights tool.
2. Confirm next action: As per scratch pad remaining goals and task, call the 'flights' tool with appropriate parameters: from_city='London', to_city='Paris', date='2025-05-29'.
3. Prepare tool call: Use the exact params_schema for 'flights' tool.
4. Update scratch pad: Advance the state to reflect the tool call. Update current step to awaiting flights tool result. Adjust remaining goals, progress to Step 3 of 4 (previously Step 2 of 4). Keep notes relevant.
5. Output format: Provide the tool call in a parseable ReAct-like format for agent loop compatibility, followed by the updated scratch pad content. This performs the task directly and efficiently.
6. Ensure precision: Cities use names as specified in user request (tool accepts name or code). No further tools needed yet; await result before summarizing.

Response: Action: flights
Action Input: {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"}

Updated scratch_pad:
Current step: flights tool called with parameters (London to Paris on 2025-05-29). Awaiting tool result to interpret and store.
Remaining goals:
- Interpret 'flights' tool results.
- Compile and present the flight information to the user.
Notes:
- Date format confirmed as YYYY-MM-DD.
- Store flight search results in working memory for final summary.
Progress: Step 3 of 4.

current_cycle: 4