AI can draft the dataloader refactor, but a human engineer has to verify it actually works.

N+1 detection and dataloader batching follow recognizable patterns in Elixir/Phoenix, making the audit structurally repeatable. But each resolver has unique associations and context, so the agent must make per-resolver judgment calls rather than applying a single template.

The goal — reduce response time from 3–5s — is measurable, but success requires runtime benchmarking the agent cannot perform. The agent can produce code changes but cannot confirm whether the target latency is actually achieved.

The agent needs full access to the resolver source files, schema definitions, and ideally query logs or Ecto query traces — all of which may or may not be provided. Without live database access or query plan output, the agent is working from static analysis alone.

Incorrect batching logic can introduce subtle bugs — wrong data associations, missing preloads, or silent data corruption — that may not surface immediately in tests. Changes are reversible via version control, but bad code reaching production is a real risk.

Choosing between eager loading, dataloader batching, or query restructuring involves trade-offs around cache behavior, concurrency, and business logic that benefit from human expertise. A senior engineer is needed to validate the approach and run integration tests.