Data Lake for Agentic AI
BetaYour data lake,
ready for AI agents
AI agents are becoming primary consumers of SQL infrastructure. LakeOps is the control plane that makes your lake intelligent — agent-native interface, built-in guardrails, self-optimizing storage, and a closed-loop feedback system that learns from every query.
The challenge
AI agents deserve a data lake built for them
Agents hit slow, stale tables
AI agents issue SQL expecting sub-second latency. Uncompacted tables with small files and bloated manifests force full scans, turning a simple lookup into a 10× slower query.
No guardrails for autonomous SQL
Agents running unsupervised can issue DDL, scan petabytes, or expose PII. Without layered safety policies, every agent query is a compliance and cost risk.
Wrong engine, wrong cost
An interactive agent query landing on a batch engine wastes minutes and compute. Without workload-aware routing, agents can't pick the fastest or cheapest path.
No feedback loop
Agent workloads change constantly, but tables stay static. Without a closed-loop system that feeds query signals back to the optimizer, the lake never adapts to how agents actually use it.
Capabilities
Everything agents need from your lake
A unified control plane purpose-built for agentic workloads — connection, safety, routing, optimization, and observability.
Agent-native MCP interface
Native MCP server connects any compatible agent — Claude, LangChain, or custom — with zero integration code. Schema-aware tools, async queries with SSE streaming, and Postgres/MySQL/Arrow Flight wire compatibility.
Layered safety guardrails
ReadOnlyGuard blocks DDL, CostEstimateGuard rejects expensive scans, PIIMaskGuard scrubs sensitive columns, HumanApprovalGuard pauses high-stakes queries. Stack them per agent, per team, or globally.
Intelligent multi-engine routing
Three-router stack — Adaptive routes on history, LLM reasons over new templates with live table stats, Semantic matches intent. 0ms cached decisions, data-quality-aware routing enriched by table health.
Self-optimizing storage
Agents querying uncompacted tables pay 5–10× latency penalty. The workload analyst feeds agent query signals to the Rust compaction engine, and the feedback loop auto-updates routing as tables improve.
Query observability & audit
Every agent query is logged, attributed, and explainable. See which agents query which tables, what guardrails fired, which engine was chosen, and why — with full cost and latency attribution.
Closed-loop feedback
Agent workload patterns flow back to the optimizer: hot tables get compacted first, partitions evolve to match access patterns, and routing weights adjust automatically as data quality improves.
Agentic AI enablement
Live routing control
for autonomous workloads
Bring the platform-grade endpoint routing experience directly into your data-lake workflow: isolate workloads, enforce policy, and continuously tune where agent queries run.
Groups
4
Configured endpoints
Active
3
Passing traffic
Inactive
1
Paused groups
Endpoints
8
Publicly available
All routing groups
Interactive customer behavior and funnel exploration workloads.
Operational writes and near-real-time checkout event ingestion.
Nightly catalog transformations and product availability sync jobs.
Scheduled BI reports and financial dashboard refresh workloads.
Cost-effective scale
Lower cost per agent query,
built for efficient scale
This page is about cutting cost, and this is where it compounds: optimized Iceberg tables let agents run more tasks with less query compute and more predictable spend.
Lower token-to-query cost
Faster, cleaner tables let agents finish tasks with fewer retries and lower compute per query.
Agent-safe optimization loop
Every optimization is simulated and validated before promotion, so autonomous workloads scale without cost spikes.
Cost-effective AI scale
As agent traffic grows, adaptive compaction and routing keep latency stable and infrastructure spend predictable.
How it works
From agent query to optimized result
A closed loop: agents connect, policies enforce, routers decide, and the lake self-optimizes — continuously.
Agents connect via MCP
Any MCP-compatible agent — Claude, LangChain, custom builds — connects to LakeOps with zero integration code. Schema-aware tools are auto-discovered.
Guardrails enforce policy
Every query passes through stackable guards — read-only, cost ceiling, PII masking, human approval — before it reaches an engine.
Router picks the engine
Adaptive, LLM, and semantic routers choose the fastest and cheapest engine based on query shape, table health, and history. Cached decisions take 0ms.
Lake self-optimizes
Agent query signals feed back to the Rust compaction engine. Hot tables compact first, partitions evolve, and routing weights update as data quality improves.
Make your lake agent-ready today
See how LakeOps connects AI agents to your Iceberg tables — safely, fast, and autonomously.