Rewriting a slow Go pipeline for concurrency is squarely in AI's wheelhouse.

The problem structure is fixed: read from Kafka, deduplicate by composite key, write to ClickHouse. There is no novel judgment required each time this runs; the pipeline logic is deterministic and repeatable.

Success criteria are concrete and measurable: deduplication correctness on the composite key and end-to-end runtime under 30 minutes on 2–5 GB daily input. An agent can reason clearly about whether a proposed design meets these targets.

The agent can generate idiomatic Go code using standard concurrency primitives and well-known Kafka/ClickHouse client libraries, but it does not have live access to the existing codebase, schema definitions, or cluster configs. A human must supply those and wire in the generated code.

Incorrect deduplication logic could silently drop valid events or pass duplicates into ClickHouse, corrupting downstream analytics. However, the pipeline can be tested against a sample dataset before production deployment, making errors detectable and reversible before real damage occurs.

Concurrency patterns, batching strategies, and ClickHouse bulk-insert tuning are well-documented engineering decisions with established best practices. No subjective taste or relationship context is needed; this is a technical optimization problem AI handles well.