Agents – anthropic/claude-sonnet-4.6 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

11s

Input Tokens

1504

Output Tokens

541

Cost

$0.01

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: The task asks me to evaluate the complexity of a flight search task. Let me analyze it:

1. **Task description**: Find available flights from London to Paris for tomorrow.
2. **Steps involved**:
   - Step 1: Get today's date using the calendar tool
   - Step 2: Interpret the calendar result (2025-05-29 = today, so tomorrow = 2025-05-30)
   - Step 3: Query the flights tool with from=London, to=Paris, date=2025-05-30
   - Step 4: Present the results to the user
3. **Tools needed**: Calendar tool + Flights tool = 2 tools, both available
4. **Reasoning required**: Minimal — just a date increment and a direct tool call
5. **Number of steps**: 4 total steps, small number
6. **Fallback/adaptive planning needed**: None mentioned; tools are available and working

This clearly fits the 'Easy' category: small number of clear steps, dedicated tools exist, minimal reasoning needed — which matches the example given ('find tomorrow's date and query flights').

Response: **Complexity: Easy**

This task falls into the **Easy** complexity category for the following reasons:

1. **Small number of steps (4 total)**: The task is broken into a clear, linear sequence — get today's date → calculate tomorrow's date → query the flights tool → present results. The agent is already at Step 2 of 4, with no unexpected detours.

2. **Dedicated tools exist**: Both required tools (`calendar` and `flights`) are explicitly available and operational. No workarounds or alternative tools are needed.

3. **Minimal reasoning required**: The only reasoning needed is a simple date increment (+1 day from the calendar result). The flight query parameters (from: London, to: Paris, date: 2025-05-30) are straightforward and fully derivable from the user's request.

4. **No adaptive planning or fallbacks needed**: There is no missing data, no tool failures, and no need for multi-source coordination or contingency strategies.

This is essentially the textbook example of an **Easy** task: find tomorrow's date and query flights — which directly matches the provided example definition.

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: The task asks me to evaluate the complexity of a flight search task. Let me analyze it: 1. **Task description**: Find available flights from London to Paris for tomorrow. 2. **Steps involved**: - Step 1: Get today's date using the calendar tool - Step 2: Interpret the calendar result (2025-05-29 = today, so tomorrow = 2025-05-30) - Step 3: Query the flights tool with from=London, to=Paris, date=2025-05-30 - Step 4: Present the results to the user 3. **Tools needed**: Calendar tool + Flights tool = 2 tools, both available 4. **Reasoning required**: Minimal — just a date increment and a direct tool call 5. **Number of steps**: 4 total steps, small number 6. **Fallback/adaptive planning needed**: None mentioned; tools are available and working This clearly fits the 'Easy' category: small number of clear steps, dedicated tools exist, minimal reasoning needed — which matches the example given ('find tomorrow's date and query flights'). Response: **Complexity: Easy** This task falls into the **Easy** complexity category for the following reasons: 1. **Small number of steps (4 total)**: The task is broken into a clear, linear sequence — get today's date → calculate tomorrow's date → query the flights tool → present results. The agent is already at Step 2 of 4, with no unexpected detours. 2. **Dedicated tools exist**: Both required tools (`calendar` and `flights`) are explicitly available and operational. No workarounds or alternative tools are needed. 3. **Minimal reasoning required**: The only reasoning needed is a simple date increment (+1 day from the calendar result). The flight query parameters (from: London, to: Paris, date: 2025-05-30) are straightforward and fully derivable from the user's request. 4. **No adaptive planning or fallbacks needed**: There is no missing data, no tool failures, and no need for multi-source coordination or contingency strategies. This is essentially the textbook example of an **Easy** task: find tomorrow's date and query flights — which directly matches the provided example definition.