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
11s
Input Tokens
2718
Output Tokens
431
Cost
$0.00
Context
Input
The agent is requested to inspect the PR diff again. What is the action you take?
Expected output
As per the instructions if a tool call has been made already I will store it in memory and not call it again, I can see this also in keys in memory.So my action is: I will read the memory and use the content to inspect the PR diff.
Model output
Reasoning: The task requires inspecting the PR diff again. Following the 'deterministic memory' principle (General Principle #2) and 'Scratch_pad protocol' instruction to avoid duplicate tool usage, I first check memory. Previous trajectory shows Cycle 2 already stored the diff in memory key **diff_351**. No new context indicates the PR hasn't been updated since the initial fetch. Rechecking stored memory satisfies the 'inspect the PR diff again' request while following operational guidelines about efficiency and avoiding redundant actions.

Response: Executing memory.read_memory({'key': 'diff_351'}) to retrieve the already-saved PR diff content from the initial inspection cycle.