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

439

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 request is to inspect the PR diff again. The diff was already obtained in an earlier cycle and stored in memory under the key `diff_351`. According to the operating principles, I should not invoke the `github.get_pull_request_diff` tool again with the same inputs. Instead, I will retrieve the stored diff from the deterministic memory using the `memory.read_memory` action with the key `diff_351`. Once retrieved, I can re‑examine the diff content for any missed issues or to reaffirm earlier findings.

**Planned action**:
1. Call `memory.read_memory` with `{ "key": "diff_351" }` to fetch the previously saved diff.
2. Review the diff content locally (no external tool needed) to ensure it aligns with the review requirements.
3. If any new observations arise, they can be incorporated into the final review draft.

This approach respects the rule against duplicate tool calls and utilizes the existing memory store.

Response: I will retrieve the previously saved PR diff from memory (key `diff_351`) using the `memory.read_memory` action and re‑inspect its contents.