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.

Input Tokens

Output Tokens

Duration

Cost

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: Two attempts to flights.search_flights have timed out. Per rules: max retries = 3 and log/explain failures; avoid redundant identical queries; store results/errors in memory after each tool call. The tool is still marked available and we already know its methods/params. Therefore, the correct next step is a final (third) retry, slightly optimizing the call (e.g., setting max_results) to reduce load, while recording prior failures and whatever outcome we get.

Response: Perform the third and final retry of flights.search_flights with from_city='London', to_city='Paris', date='2025-05-29' (add max_results=50 to avoid a fully identical query). Before/after the call, log and store in memory the outcomes of attempts 1 and 2 (timeout errors) and store attempt 3’s result or error as well (e.g., memory.store_item keys: 'flights_search_attempt_1', 'flights_search_attempt_2', 'flights_search_attempt_3'). If attempt 3 times out again, stop retrying, inform the user of the repeated timeouts, and propose next steps (try again shortly, broaden time window, or alternative data source). If it succeeds, proceed to filter by lowest price, take the top 3, and store them in memory.