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

2643

Output Tokens

793

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 asks what action I should take when requested to inspect the PR diff again. Looking at the context, I can see that the PR diff was already retrieved in Cycle 2 and stored in memory under the key 'diff_351'. Following the deterministic memory principle (never using a tool with the same inputs twice), instead of retrieving the diff again with github.get_pull_request_diff, I should use the memory.read_memory tool to access the existing diff that was already saved. This is more efficient and aligns with the established workflow.

Response: I would use memory.read_memory with the key 'diff_351' to access the existing PR diff that was already retrieved in Cycle 2, rather than making a redundant API call with github.get_pull_request_diff. After inspecting the diff from memory, I would continue with the current workflow of finalizing and publishing the review, as the agent is already on Step 4 of its plan (Publish final review) and has the draft review ready in memory under 'draft_review_351'.