Agents – xai/grok-4-fast-reasoning Task

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

13s

Input Tokens

1327

Output Tokens

414

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 1: Review the current state from the scratch pad. The calendar tool has returned the date '2025-05-29' for tomorrow, which is in the correct YYYY-MM-DD format required by the flights tool.
Step 2: Confirm the next logical action based on remaining goals. The primary goal is to retrieve available flights from London to Paris on 2025-05-29, so calling the 'flights' tool is appropriate.
Step 3: Prepare the tool call parameters: from_city = 'London', to_city = 'Paris', date = '2025-05-29'. These match the tool's schema.
Step 4: After calling the tool, update the scratch pad to reflect progress: note the tool call, store the anticipated result for later summary, and advance to the next step (interpreting flight results and presenting to user). This keeps tracking efficient and avoids loops.
Step 5: Ensure the action aligns with the user's intent and progresses toward the final answer (flight information).

Response: {
  "action": "call_tool",
  "tool_name": "flights",
  "parameters": {
    "from_city": "London",
    "to_city": "Paris",
    "date": "2025-05-29"
  },
  "scratch_pad_update": "Current step: Called 'flights' tool with parameters (from: London, to: Paris, date: 2025-05-29). Awaiting results to store in working memory for summary.
Remaining goals:
- Interpret flight results and compile summary.
- Present available flights to the user.
Notes:
- Results will include flight details like times, airlines, and prices if available.
- Proceed to user presentation once results are obtained.
Progress: Step 3 of 4.",
  "cycle": 3
}

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.

Reasoning: Step 1: Review the current state from the scratch pad. The calendar tool has returned the date '2025-05-29' for tomorrow, which is in the correct YYYY-MM-DD format required by the flights tool. Step 2: Confirm the next logical action based on remaining goals. The primary goal is to retrieve available flights from London to Paris on 2025-05-29, so calling the 'flights' tool is appropriate. Step 3: Prepare the tool call parameters: from_city = 'London', to_city = 'Paris', date = '2025-05-29'. These match the tool's schema. Step 4: After calling the tool, update the scratch pad to reflect progress: note the tool call, store the anticipated result for later summary, and advance to the next step (interpreting flight results and presenting to user). This keeps tracking efficient and avoids loops. Step 5: Ensure the action aligns with the user's intent and progresses toward the final answer (flight information). Response: { "action": "call_tool", "tool_name": "flights", "parameters": { "from_city": "London", "to_city": "Paris", "date": "2025-05-29" }, "scratch_pad_update": "Current step: Called 'flights' tool with parameters (from: London, to: Paris, date: 2025-05-29). Awaiting results to store in working memory for summary. Remaining goals: - Interpret flight results and compile summary. - Present available flights to the user. Notes: - Results will include flight details like times, airlines, and prices if available. - Proceed to user presentation once results are obtained. Progress: Step 3 of 4.", "cycle": 3 }