Agents – fireworks/deepseek-r1 Task | Opper AI

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

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Output Tokens

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Context

Input

The flights.search_flights call has returned 6 available flights from London to Paris for tomorrow. What is the next tool you will call, and why?

Expected output

The next tool is memory, to store the flights tool call result.

Model output

Reasoning: The user's goal is to get the cheapest flights. Since the flights.search_flights call returned 6 options, the next step is to filter these by price. The flights tool has a filter_by_price method for this purpose. Following the instructions to chain tools and store results, calling filter_by_price will process the raw flight data into a ranked list by cost, which can then be reduced to the top 3 options (via get_cheapest_option or explicit sorting) before storing in memory.

Response: The next tool is flights.filter_by_price. Rationale: The user requires the cheapest flights, and filtering/sorting by price is the logical next step after retrieving raw flight data. This follows the trajectory's planned step of 'Filter by price and select top 3' with available flight results to work with.