Good AI Task

AI compatibility

AI can draft the optimization, but it needs real database access to actually fix the timeout.

Possible with caveats

Workable, but read the conditions.

Average across 1 submission.

62
avg / 100

The honest read

An AI code agent can handle the mechanical parts of this task well — profiling queries, adding select_related/prefetch_related, suggesting indexes, and scaffolding cursor-based pagination — but the real bottleneck is access: the agent needs live database access, the actual codebase, and query execution results to do this properly. Without those, it produces plausible-looking but unvalidated code that may miss the actual performance bottleneck entirely.

Aggregated across 1 submission.

The five dimensions

Repeatability

Medium

The general pattern — profile, optimize ORM queries, add indexes, implement cursor pagination — is structurally repeatable. However, the specific joins, data shapes, and bottlenecks vary per schema, so each instance requires schema-specific judgment.

Ambiguity Tolerance

Medium

Success criteria are partially clear (endpoint no longer times out, pagination works), but 'optimized' is relative — acceptable query time, index choices, and pagination UX tradeoffs require human sign-off to confirm the bar has been met.

Data & Tool Availability

Low

The agent needs the actual codebase, schema definitions, EXPLAIN ANALYZE output, and ideally a staging database to test against. Without these, it can only produce generic recommendations that may not address the real bottleneck.

Error Cost

Medium

Incorrect indexes or ORM changes can degrade write performance or introduce subtle query bugs. Changes to a production API endpoint carry real risk, though a staging environment and code review can contain most damage.

Human Judgment Required

Medium

Choosing which indexes to add, whether to denormalize, and how to handle pagination edge cases (e.g., cursor stability under concurrent writes) involves tradeoffs that benefit from a senior engineer's review, though the agent can produce a strong first draft.

What an agent would need

  • Full read access to the Django codebase, including models, serializers, and the problematic view
  • Database schema details and the ability to run EXPLAIN ANALYZE on the slow query
  • A staging or development environment to test query changes and measure actual performance
  • Ability to write and commit code changes (or at minimum produce a diff for human review)
  • Knowledge of the acceptable response time SLA and any API contract constraints (e.g., cursor format, backward compatibility)

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Best-matched agent

Code Agent

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