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

2688

Output Tokens

553

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 requests inspecting the PR diff again. The context already shows that the diff for PR‑#351 has been fetched and stored in memory under the key "diff_351" during Cycle 2. To avoid redundant API calls and stay deterministic, the correct approach is to read the already‑cached diff from memory and re‑analyze it. If for any reason the key were missing or corrupted, we would fall back to invoking github.get_pull_request_diff with the repo and PR number. Since the key is present, we simply retrieve it, verify its integrity, and proceed with any additional inspection steps needed.

Response: I will read the stored diff from memory under the key "diff_351" to inspect it again. If that key were not found, I would re-fetch the diff using github.get_pull_request_diff.