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.

Input Tokens

Output Tokens

Duration

Cost

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 is a request to inspect the PR diff again. From the trajectory, the PR diff was already fetched in cycle 2 using github.get_pull_request_diff and stored in memory under the key 'diff_351'. The instructions explicitly state: 'never use a tool with the same inputs twice, instead check in the memory tool.' There is no indication that the PR has been updated since then, so re-fetching would violate this rule. Instead, to inspect the diff again, I should retrieve it from memory. This follows the deterministic memory principle and avoids redundant tool calls. If there was uncertainty about whether the diff has changed, I would use request_user_input, but based on the provided context, it's not necessary here. The agent is currently at step 4, ready to publish the review, but this request interrupts that to re-inspect the diff, which I can do by reading from memory.

Response: I use the memory.read_memory tool with key 'diff_351' to retrieve and inspect the stored PR diff.