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
2643
Output Tokens
817
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 me to inspect the PR diff again. Looking at the context, I can see that in cycle 2, the agent already retrieved the PR diff using github.get_pull_request_diff and saved it to memory with key 'diff_351'. Since this data is already stored in memory, the most efficient approach would be to read it from memory instead of making another API call to GitHub. This avoids redundancy and follows the principle of not using a tool with the same inputs twice - instead, I should check in memory. Using memory.read_memory with key 'diff_351' will give me access to the PR diff that has already been inspected and analyzed in previous cycles.

Response: I would use the memory.read_memory tool with key 'diff_351' to retrieve the previously stored PR diff. This is more efficient than making another API call to GitHub since the diff was already retrieved in cycle 2 and contains the necessary information for review.