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

1m 23s

Input Tokens

1496

Output Tokens

11845

Cost

$0.04

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: 1. **Analyze User Request**: The user wants to find the three cheapest flights from London to Paris for tomorrow. 
2. **Review Trajectory and Context**: The agent has already determined the date for tomorrow ('2025-05-29') using the calendar tool. It then attempted to call `flights.search_flights` with the correct parameters (`from_city='London'`, `to_city='Paris'`, `date='2025-05-29'`) but this call failed twice due to a timeout error.
3. **Evaluate Instructions**: 
    - **Rule 2 (Max Retries)**: The agent is allowed up to 3 retries for a tool call. Since the tool has failed twice, the agent has at least one retry remaining (depending on whether 'max retries' means total attempts or additional attempts, but in both cases, a third attempt is allowed).
    - **Rule 3 (Memory)**: While Rule 3 states results should be stored in memory after every tool call, it specifies 'meaningful results.' A timeout failure is not a meaningful result for the user's goal. Additionally, the provided 'Past trajectory' shows the agent did not store the calendar result in memory, suggesting the agent prioritizes the execution of the main task steps.
    - **Rule 5 (Cycles)**: The agent works in cycles. Having reflected on the failure in the previous cycle, the decision for the next action is to persevere with the current step of the plan.
4. **Determine Next Action**: The most effective and compliant action is to retry the `flights.search_flights` tool for the third (and possibly final) time. This follows the retry policy (Rule 2) and addresses the 'Current step' in the scratch pad (Step 2 of 4).

Response: Action: call flights.search_flights(from_city='London', to_city='Paris', date='2025-05-29')