When Planck scaled its LLM‑agent fleet to hundreds, latency jumped not because of the LLM APIs but due to a flood of tiny async CPU tasks. The authors pinpointed the hidden CPU contention and rewrote the dispatcher to eliminate the bottleneck, restoring fast response times.
Public benchmarks report about 80% accuracy for Text‑to‑SQL models, but Michael Stonebraker’s test on a 1,400‑table Oracle warehouse yielded only ~10% accuracy, rising to ~30% with explicit table hints. The gap stems from unseen proprietary data, far more complex queries, idiosyncratic naming, and tangled schemas. Teams risk over‑estimating AI tools if they rely on clean‑benchmark scores.
A new AI Impact Analyst watches Iceberg tables via Lakekeeper, captures schema edits in Kafka, and uses an Anthropic model to assess downstream impact within seconds. The service posts real-time alerts and remediation tips to Slack, cutting the costly downtime that schema drift normally triggers.
Mid‑2026 production pipelines now deploy specialist agent teams, triage, SQL, Python, tied together by the Model Context Protocol (MCP) and linked to GitHub, Slack, PostgreSQL. Persistent memory graphs harvest execution histories, letting agents build lasting knowledge and eliminating brittle orchestrated reasoning loops. The result is faster, more reliable AI‑driven workflows.