Backed by

The agent layer for enterprise data

AgentLake sits between your agents and your warehouse. Every query is grounded in context, answered from verified cache when it can be, attributed to a person, and governed by your access policies.

Schedule a demo15 minutes, on your own stack.

agent traceworkspace · acme

Claude Code·maya@acme.com

“Gross margin by product line, last four quarters?”

context · finance.margins + margin definition0.3s

policy · executed as maya@acme.com0.1s

warehouse · Snowflake scan2.9s

capsule · verified result written · c_9f2e0.2s

answered in 3.5s· provenance attached

Cursor·daniel@acme.com

“Gross margin by product line, last four quarters?”

context · same grounding resolved0.2s

policy · executed as daniel@acme.com0.1s

capsule · hit · verified 4m ago · c_9f2e0.1s

answered in 0.4s· same answer, no second scan

Fig. 1 · Agent trace

Connectors across warehouses, databases & tools

Execution accuracy on the Spider benchmark

0.0%

Spider leaderboard best: 91.2% (MiniSeek)

Agent actions covered by audit and approval

Every run traced; risky writes wait for sign-off

Every query cached, observed, governed

Claude Code, Cursor, Slack, or the web app: whatever asks passes through the same layer and gets the same three guarantees.

Slack

Claude Code

Your IDE

Web App

Answer once, reuse everywhere

The first agent to ask pays for the warehouse scan. The answer is written back as a verified capsule with its grounding attached; every agent that asks after that gets the same answer in milliseconds, not a second scan on your bill.

capsule · c_41d8verified · 2h old

“Weekly active users by workspace, trailing 12 weeks?”

prod.events.active_user

definition · weekly_active

served without a second scan

Claude Code · maya@0.4s

Cursor · daniel@0.4s

Slack · priya@0.4s

Every query has a name on it

Each run is attributed to the agent that made it and the person it ran as, with the full trace behind it: the prompt, the context it resolved, the SQL, the result.

2m agomaya@acme.com · Claude Codefinance.margins

9m agodaniel@acme.com · Cursorprod.events

31m agopriya@acme.com · Slackprod.accounts

The person who asked sees a “Ran as you” badge on the result; the data team sees the whole ledger.

Agents inherit your access model

Agents run with the credentials of the people they work for, approvals gate risky writes, and every action leaves an audit trace.

IdentityPer-user credentials; queries run as the person who asked

LineagePrompt to exact SQL result

ControlWrites wait for human approval

AccessStrict RBAC, enforced and written back to the warehouse

ComplianceSOC 2 Type 2 in progress

DeploymentManaged cloud, private VPC, or fully on-prem

Underneath: one context graph

AgentLake reads your schemas, dbt models, and definitions, then enriches them with context from GitHub, Slack, and your BI tools. Every answer is grounded here, every capsule records exactly which of it it used, and access decisions resolve against it. The lake is why the layer above can be fast and still be right.

prod.orders.amount_usdcontext attached

dbt · finance/revenue.sql

#data-eng: “net of refunds”

Fig. 2 · Context graph

What teams run on it

Dashboards, autonomous insights, and pipelines all draw from the same layer, so every surface agrees with every agent.

Revenue overviewrefreshing…

Net revenue

$184k

+6.2% WoW

Orders by week

ARR by segment Enterprise Mid-market

Fig. 3 · Dashboard refresh

Dashboards

Tiles render straight from your warehouse and refresh themselves on schedule. Freshness and refresh health stay visible on every data source.

Refreshes on schedule Freshness per source

Enterprise churn down 1.2 pts WoW92%

stripe.subscriptions × prod.accounts: the EU cohort drove the drop

sent to #leadership

Checkout latency up 18% since v2.487%

p95 from events.checkout: regression began with Tuesday's deploy

sent to #eng-alerts

Q4 pipeline coverage at 2.1×78%

hubspot.deals against quota targets, weighted by stage

sent to #revenue

Fig. 5 · Insights, delivered

Autonomous insights

Agents keep watching between questions and surface findings with evidence and a confidence score, delivered where your team already reads.

Confidence scored Delivered in Slack

dbt buildon merge

dbt-build-2e8d

compile 412 models3.1s

run 38 tests6.4s

materialize incremental2.9s

completed in 12.4s· 0 retries

recent runs

dbt buildjust now

Dashboard refresh1h ago

Catalog sync6h ago

Fig. 4 · Pipeline run

Pipelines

Catalog sync, dashboard refresh, and transformations run as durable scheduled workflows. Every run, step, and retry stays visible.

Durable workflows Catalog sync