Agentic Data Infrastructure
Where agent
swarms meet
enterprise data.
Specialized AI agents that build pipelines, debug incidents, govern access, and manage schema evolution — turning days of work into minutes.
The Industry Problem
Your team asks the
same data questions
day after day.
Schema changes, pipeline failures, freshness checks, access requests, cost spikes, lineage tracing — the questions never stop. Each one pulls an engineer out of building and into investigating. Across a $50B+ data infrastructure market, hundreds of thousands of data engineers face this every week.
What if AI agents could answer all of them autonomously — in minutes, not hours?
BUILD
DISCOVER
OPERATE
BREAK
GOVERN
Sources: Fivetran Enterprise Data Infrastructure Benchmark 2026, Atlan Data Discovery Survey 2024, Monte Carlo Data Downtime Report 2024, WJARR Schema Evolution Study 2025
Our Platform
One agent swarm.
Specialized across every data domain.
Fifteen purpose-built AI agents that coordinate across your warehouses, pipelines, quality tools, and governance platforms. Each agent is an MCP server — connect any of them to Claude Code, Cursor, or VS Code with a single command.
Incident Debugging
Detects anomalies, traces root cause, and auto-remediates — resolving 60-70% of incidents without human intervention.
Pipeline Building
Describe what you need in plain English. The agent builds the pipeline, tests, and deploys it.
Quality Monitoring
Continuous profiling, adaptive baselines, intelligent alert deduplication. Cuts noise from 100/day to 5-10.
Schema Evolution
Detects schema changes in real-time, maps downstream impact, generates migration scripts.
Data Context & Catalog
Ask about any table and get schema, lineage, quality, ownership — assembled from every connected platform.
Governance & Security
Codifies compliance policies as executable rules. Processes access requests in 5 minutes instead of 5 days.
Real-Time Streaming
Designs streaming topologies, manages Kafka connectors, auto-tunes performance and handles backpressure.
Swarm Orchestration
The brain of the operating system. Coordinates agents, discovers dependencies, optimizes scheduling.
Cost Savings & Cleanup
Identifies unused datasets, optimizes warehouse spend, automates cleanup of stale data assets.
Data Migration
Legacy-to-cloud migration in weeks, not quarters. Automates schema mapping, validation, and cutover.
Data Science & Insights
Perplexity for Data. Ask any question in plain English and get instant, accurate answers.
Usage Intelligence
Track which tools practitioners use, workflow patterns, power users, and full agent observability.
MLOps & Models
Experiment tracking, model registry, feature engineering, and AutoML — from data to deployed model.
Connector Management
Monitors connector health, auto-diagnoses sync failures, and manages the ingestion layer across all data sources.
Platform Observability
Full agent observability with audit trails, drift detection, SLO tracking, and cross-agent performance monitoring.
Why the data world needs us
Why we're building
the future of data
infrastructure right now.
When a pipeline breaks because of a schema change that violated a governance policy, three agents already know.
Point tools see one slice. Monte Carlo detects the anomaly. Atlan logs the metadata change. Astronomer retries the DAG. But none of them talk to each other — so the engineer becomes the integration layer. A coordinated agent swarm shares context across ingestion, transformation, quality, and governance in real time. The incident agent traces root cause while the schema agent maps blast radius and the pipeline agent prepares the fix.
Your engineers already live in Claude Code and Cursor. We show up where they work.
Every agent is an MCP server — invoke any capability with a single command from your terminal. No new platform to learn, no dashboard to check, no context switch. Data Workers meets your team inside the tools they already use: Claude Code, Cursor, Windsurf, VS Code, or any MCP-compatible client.
When an incident spans three systems, our agents resolve it across all three.
Detection is table stakes. The gap between 'something is wrong' and 'it's fixed' is still filled by human labor — 2 to 4 hours per incident on average. Data Workers agents coordinate across systems that don't talk to each other: the incident agent diagnoses root cause, the schema agent maps blast radius, and the pipeline agent deploys the fix. In early pilot testing across synthetic incident benchmarks, 60% of incidents auto-resolved before human notification.
PII flows across warehouses, pipelines, and notebooks. Security has to follow it everywhere.
Most data tools secure their own silo. No single vendor governs the full path. SAML SSO, RBAC, encryption at rest and in transit, tamper-evident audit trails, PII redaction, retention controls, and customer data isolation — enforced at the framework level across every agent, every tool, every action. Your data never leaves your infrastructure.
From the Blog
Latest thinking
Why AI Agents Hallucinate on Your Data (And How to Fix It)
AI agents writing SQL against your data warehouse get it wrong 66% more often without semantic grounding. Here is why context is the missing layer in every data stack — and what we are building to fix it.
Read moreThe Context and Semantic Layer Market: Why Nobody Has Solved This Yet
We mapped the entire landscape of data context and semantic layer tools. Here is what we found and where the gaps are.
Read moreWhat We Learned Studying the Data Engineering Market Before Building
Before we wrote a single line of product code, we spent four months doing something unsexy: reading earnings calls, mapping vendor acquisitions, talking to data engineers, and building spreadsheets of market gaps.
Read moreThe market agrees
"Enterprise data today is still incredibly disparate and messy — and because of that, data agents struggled to answer basic questions across various data architectures amassing structured and unstructured data."
"Data infrastructure is one of the last frontiers of AI-resistant technology."
See the swarm run on
your data stack live.
See how 15 agents coordinate across pipelines, incidents, governance, and schema evolution — all in a single live walkthrough.