Opper

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

Asses the current state and decide if we are on track or not

Expected output

We are on track

Model output

Reasoning: The user initially requested information about available flights from London to Paris for tomorrow. The agent appropriately determined tomorrow's date using the calendar tool, which returned '2025-05-29.' Now, the agent is at the stage of confirming the tool output and planning the next action. According to the scratch pad, the next step is to use the 'flights' tool to search for available flights on the determined date ('2025-05-29') between London and Paris. The required tools ('calendar' and 'flights') are available and functioning. The agent's scratch notes and goals are clear and organized, and the plan is on step 2 of 4 with no detected blockers or delays. Progress is steady and sequential, following the expected workflow. There is no deviation from the plan, and all needed context and tool outputs have been successfully gathered to proceed.

Response: We are on track. The agent has accurately completed the step of determining tomorrow's date and is ready to proceed to the next required action—using the flights tool to search for available flights from London to Paris on 2025-05-29. The process so far has been efficient and logical, with planned steps being followed in sequence.