Agent Access
One MCP endpoint over the whole lake.
CtrlB does not sell you an agent. It exposes a Model Context Protocol interface over the same data layer the UI queries, so the agent you already trust reads complete operational history directly — no dashboard scraping and no export pipeline to keep in sync.
Let your agents investigate without limits.
Traditional platforms sample traces, cap cardinality, age out logs, and throttle queries — walls that keep a vendor’s bill flat at human query volumes. An agent investigates at machine scale and hits every one on the first incident. CtrlB has none of them: reads are unmetered, history stays whole, and the only limit left is the evidence itself.
- Queries completed
- 3
- Dimensions explored
- 2
- History compared
- 14d cap
- Rate-limited
- 1
- Elapsed
- 00:19
The wall isn’t a bug — it’s how the vendor keeps its own bill flat.
- Queries completed
- 3,442
- Dimensions explored
- 36
- History compared
- 90 days
- Rate-limited
- 0
- Elapsed
- 00:04
payments-api v84 introduced a retry storm after deployment.
Connect the agent you already use
CtrlB speaks native Model Context Protocol, so any MCP client reads the same lake the UI queries — no bundled assistant, no lock-in.
Native MCP support
Structured access to the whole lake through Model Context Protocol. Works with Claude Code, Cursor, and any MCP client.
Docs: MCPSame lake as the UI
Agents and engineers read the same open Parquet. No dashboard scraping, no export pipeline to keep in sync.
Data EngineBuilt for AI workloads
Sub-second queries and unmetered reads so agents can iterate freely — twenty questions instead of three.
PlaygroundWhat it gives every agent
- Bring your own agent; the interface is open rather than a bundled assistant.
- Sub-second responses, so an agent can ask twenty questions instead of three.
- Agents and engineers read the same evidence from the same files.
- Sustained query volume is a compute decision, not a licensing tier.
What you can build
Real workflows when agents can query freely — without scraping dashboards or standing up a second copy of the data.
Natural-language investigation
“Why did checkout latency spike after the 09:12 deploy?” The agent translates intent into queries, walks the evidence, and returns a root cause — no query language required.
- correlates with payments-api v84 rollout
- contained to us-east, already recovering
- matches incident #4821 signature
Alert triage automation
The agent reads an alert, pulls related history, checks past incidents for context, and drafts a verdict — escalating real threats, suppressing the noise, before a human opens the UI.
SELECT l.msg, m.p99, t.duration FROM logs l JOIN metrics m USING (ts, service) JOIN traces t USING (trace_id) WHERE service = 'checkout'
Cross-signal correlation
One endpoint over logs, metrics, and traces. Joins that used to need three tools and three export jobs are a single query against one lake.
CI and on-call bots
Wire Claude Code, Cursor, or your in-house bot to the same MCP surface — investigations that run where engineers already work, at the same speed as the UI.
Pattern-aware questions
Ask over compressed log patterns and full history instead of a sampled fifteen-day window. The agent reasons over the shape of production, then pulls raw evidence only where it matters.
Custom workflows
Notebooks, scripts, and internal agents hit the same MCP surface as the UI. Query programmatically, enrich alerts, or wire the lake into processes you already run.
Other core components
Collect & Control
Configure, update, and govern every collector and agent from one place.
Search & Investigate
Full-text search, SQL, metrics, and traces on object storage.
Analyze & Correlate
Patterns, anomalies, and correlations across your telemetry.
Give your agents the lake they deserve.
Connect Claude, Cursor, or your own bot. One MCP endpoint. Complete history. Questions that stay free to ask.