Frontier Investigation AgentsDatabase
AgenticOps is the discipline of running production cloud operations through autonomous AI agents — under team policy, with brokered credentials, sandboxed execution, deterministic data tokenization, and tamper-evident audit. CloudThinker applies that discipline to Starrocks, turning slow queries, runaway connection pools, lagging replicas, risky migrations, and backup gaps into a Detect → Analyze → Remediate → Verify loop your database team stays on top of instead of paged into.
CloudThinker's Frontier Investigation Agents investigate the signal, find the why and propose the fix, or run the safe action your policy allows. Your engineers approve, and the agents verify the outcome.
CloudThinker connects to Starrocks through brokered, short-lived credentials (never a stored password) and continuously watches the signals that predict incidents: slow-query and statement statistics, lock/deadlock waits, connection-pool utilization, replication lag, buffer/cache hit ratios, table and index bloat, IOPS saturation, and backup freshness. It flags a regression the moment a query's plan changes or lag crosses your SLO — not after the pager fires.
The agent reads the query plan, table and index statistics, and recent schema/traffic changes together to explain the root cause in plain language: a sequential scan from a dropped index, a hot row causing lock contention, a pool sized below concurrency, or a long-running transaction blocking vacuum/compaction. Every query it inspects runs against tokenized data — real values never leave the Starrocks boundary — so analysis is safe even on production tables holding PII.
CloudThinker drafts a concrete, reversible fix plan: the exact index to add (built concurrently), the query rewrite, the pool/timeout setting, the replica or vacuum/compaction adjustment, or the migration guarded with a lock timeout and batched backfill. Execution runs in a sandbox under graduated autonomy — L1 recommend-only, L2 apply low-risk read-path changes (a concurrent index) automatically, L3 apply write-path or config changes within policy, L4 full auto for pre-approved runbooks — with destructive operations (drops, unbounded migrations, failovers) always requiring a human approval.
After applying a change, the agent confirms it actually worked: it re-runs the offending query, compares before/after plans and p95 latency, checks that pool utilization and replication lag returned to baseline, and — for backup work — triggers a restore-test to prove RPO/RTO are met. If a fix regresses a metric, it rolls back automatically. Every step of every action is written to a tamper-evident audit trail: who connected, what ran, what data was touched, and who approved it.
$ cloudthinker db diagnose --target Starrocks-prod
DETECT orders.list p95 latency 210ms → 1.8s over 15m
└─ plan changed: Index Scan → Seq Scan on orders (5.2M rows)
└─ connection pool 96/100 in use, 41 waiting
ANALYZE root cause: idx_orders_customer_created dropped in migration 2026-07-16
hot path: SELECT * FROM orders WHERE customer_id=$1 ORDER BY created_at DESC
data access: tokenized (0 raw PII values read)
REMEDIATE fix plan (autonomy L2 — read-path, auto-approved):
1. CREATE INDEX CONCURRENTLY idx_orders_customer_created
ON orders (customer_id, created_at DESC); [reversible]
2. raise pool max_size 100 → 140, statement_timeout 30s [policy-checked]
~ HELD FOR APPROVAL: none (no destructive ops)
VERIFY index built in 47s · plan reverted to Index Scan
orders.list p95 1.8s → 190ms · pool 62/140 · 0 waiters
✓ resolved · audit: evt_9f3ac1 (engineer on the loop: notified)StarRocks management — monitor cluster health, databases, tables, materialized views, query performance, and load jobs. Use when inspecting schema layout, debugging slow queries, reviewing compaction
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Agents investigate every issue across application, cloud, security, database and Kubernetes, find the why and propose the fix. Your engineers approve, under your policies and audit trail.