Agents – xai/grok-4-1-fast-non-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

Input Tokens

1498

Output Tokens

404

Cost

$0.00

Context

Input

What is the complexity of this task?

Task complexity refers to how challenging a task is for the agent to complete, based on the number of steps required, the need for reasoning or coordination, and the availability of tools.

Examples:
- Easy: A small number of clear steps; dedicated tools exist; minimal reasoning needed (e.g., find tomorrow's date and query flights).
- Medium: Several steps; requires combining many outputs from multiple tools (more than 4); moderate reasoning (e.g., summarize document changes across multiple files and generate a review).
- Complex: Many steps; adaptive planning; potential missing data or tool failures; fallback strategies required (e.g., plan a multi-leg trip with hotel, flight, visa checks, and dynamic adjustments).

Please select the appropriate complexity level for this task and explain why.

Expected output

This task is **easy**. It only requires determining tomorrow’s date and querying available flights — both of which have dedicated, reliable tools. There’s minimal reasoning

Model output

Reasoning: Step-by-step analysis of task complexity:
1. **Understand the task**: The task is to assess the complexity of a flight search agent scenario based on provided criteria (steps, reasoning, tools).
2. **Break down the example scenario**:
   - User request: Flights from London to Paris tomorrow.
   - Steps identified: (1) Get tomorrow's date (calendar tool used, result: '2025-05-29'), (2) Call flights tool with cities and date, (3) Compile/present results.
   - Total steps: ~3-4 (date retrieval done, now interpret and call flights tool, then summarize).
   - Tools: Dedicated calendar and flights tools available; no need for web_search.
   - Reasoning: Minimal – straightforward tool chaining, date format check, no adaptive planning, no missing data, no fallbacks needed.
   - Progress: Current cycle 3, step 2 of 4, clear remaining goals.
3. **Match to examples**:
   - Matches 'Easy' example perfectly: Small number of clear steps (date → flights → present), dedicated tools exist, minimal reasoning (e.g., 'find tomorrow's date and query flights').
   - Not 'Medium' (no combining >4 tool outputs, no summarization across files).
   - Not 'Complex' (no many steps, no dynamic adjustments, no multi-leg/visa/hotel complexity).
4. **Conclusion**: This is an Easy task due to its simplicity, direct tool usage, and low reasoning demands.

Response: Easy

Explanation: The task involves a small number of clear steps (retrieve date via calendar tool → query flights tool → compile results), uses dedicated available tools with no failures or missing data, and requires only minimal reasoning like confirming date format. It directly matches the 'Easy' example provided.

What is the complexity of this task? Task complexity refers to how challenging a task is for the agent to complete, based on the number of steps required, the need for reasoning or coordination, and the availability of tools. Examples: - Easy: A small number of clear steps; dedicated tools exist; minimal reasoning needed (e.g., find tomorrow's date and query flights). - Medium: Several steps; requires combining many outputs from multiple tools (more than 4); moderate reasoning (e.g., summarize document changes across multiple files and generate a review). - Complex: Many steps; adaptive planning; potential missing data or tool failures; fallback strategies required (e.g., plan a multi-leg trip with hotel, flight, visa checks, and dynamic adjustments). Please select the appropriate complexity level for this task and explain why.

Reasoning: Step-by-step analysis of task complexity: 1. **Understand the task**: The task is to assess the complexity of a flight search agent scenario based on provided criteria (steps, reasoning, tools). 2. **Break down the example scenario**: - User request: Flights from London to Paris tomorrow. - Steps identified: (1) Get tomorrow's date (calendar tool used, result: '2025-05-29'), (2) Call flights tool with cities and date, (3) Compile/present results. - Total steps: ~3-4 (date retrieval done, now interpret and call flights tool, then summarize). - Tools: Dedicated calendar and flights tools available; no need for web_search. - Reasoning: Minimal – straightforward tool chaining, date format check, no adaptive planning, no missing data, no fallbacks needed. - Progress: Current cycle 3, step 2 of 4, clear remaining goals. 3. **Match to examples**: - Matches 'Easy' example perfectly: Small number of clear steps (date → flights → present), dedicated tools exist, minimal reasoning (e.g., 'find tomorrow's date and query flights'). - Not 'Medium' (no combining >4 tool outputs, no summarization across files). - Not 'Complex' (no many steps, no dynamic adjustments, no multi-leg/visa/hotel complexity). 4. **Conclusion**: This is an Easy task due to its simplicity, direct tool usage, and low reasoning demands. Response: Easy Explanation: The task involves a small number of clear steps (retrieve date via calendar tool → query flights tool → compile results), uses dedicated available tools with no failures or missing data, and requires only minimal reasoning like confirming date format. It directly matches the 'Easy' example provided.