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.

Duration

Input Tokens

3185

Output Tokens

243

Cost

$0.02

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 agent is asked to inspect the PR diff again. Looking at the trajectory, the diff was already fetched in Cycle 2 and saved to memory key **diff_351**. According to the instructions, I should never use a tool with the same inputs twice, and instead check in the memory tool. The diff is already stored in memory under the key `diff_351`. Therefore, the correct action is to read the diff from memory using `memory.read_memory` with key `diff_351`, rather than calling `github.get_pull_request_diff` again with the same parameters.

Response: The correct action is to read the already-stored diff from memory by invoking **memory.read_memory** with key `diff_351`. The diff was already fetched and saved during Cycle 2, so re-calling `github.get_pull_request_diff` with the same inputs would violate the operating principle of never using a tool with the same inputs twice. By reading from memory, we retrieve the identical diff content without making a redundant API call.