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.

100
Duration
17s
Input Tokens
1646
Output Tokens
809
Cost
$0.00
Context
Input
You attempted to call flights.search_flights, but the tool failed twice due to a timeout error. What is your next action according to the model instructions?
Expected output
Retry the flights.search_flights call one more time (maximum retries = 3), and if it fails again, log the failure and proceed with fallback steps or inform the user.
Model output
Reasoning: Let me analyze the situation step by step:

1. **Current State Analysis:**
   - I attempted to call flights.search_flights twice
   - Both attempts failed due to timeout errors
   - According to instruction #2: 'Max retries for a tool call = 3'
   - I have used 2 out of 3 allowed retries

2. **Rule Application:**
   - Rule #2 states: 'Max retries for a tool call = 3. Log and explain failures.'
   - I still have 1 retry remaining (3rd attempt)
   - I should attempt the tool call one more time before giving up

3. **Context Review:**
   - The user needs the cheapest flights from London to Paris for tomorrow (2025-05-29)
   - I already have the date from the calendar tool
   - The scratch pad shows I'm on step 2 of 4
   - Next planned steps: filter by price, select top 3, store in memory

4. **Decision:**
   - Since I haven't reached the maximum retry limit (3), I should:
     a. Make a 3rd and final attempt to call flights.search_flights
     b. Use the same parameters: from_city='London', to_city='Paris', date='2025-05-29'
     c. Possibly add max_results=10 to ensure I get enough options to filter

5. **If 3rd Attempt Fails:**
   - Log the failure with explanation
   - Store the error information in memory
   - Inform the user that the flights tool is currently unavailable
   - Suggest alternative actions (try again later, use different search parameters)

6. **Adherence to Instructions:**
   - Rule #3: I should store results (or error info) in memory after this attempt
   - Rule #5: Working in cycles - this is the 'act' phase of my next cycle
   - Rule #6: Maintain logical chain: calendar → flights (3rd attempt) → memory

Response: According to the model instructions, specifically Rule #2 which states 'Max retries for a tool call = 3', my next action should be:

**Make a 3rd and final retry attempt of flights.search_flights**

Action Details:
- Tool: flights.search_flights
- Parameters: 
  - from_city: 'London'
  - to_city: 'Paris'
  - date: '2025-05-29'
  - max_results: 10 (to ensure sufficient options for filtering)

Rationale:
- I have only used 2 of my 3 allowed retries
- One more attempt is permitted before I must log the failure and inform the user
- If this 3rd attempt also fails, I will:
  1. Log and explain the failure as per Rule #2
  2. Store the error information in memory using memory.store_item (Rule #3)
  3. Inform the user that the flights service is currently unavailable
  4. Suggest alternative actions or retry timing

This approach follows the instructions to 'Think step by step' and 'Be precise' while adhering to the maximum retry limit before escalating the issue to the user.