Solution
Make Every Agent a Data Agent
What It Is
In most companies, agents reach business data through stale exports and one-off connectors. Tower gives your agents governed access to enterprise data instead: an open, Iceberg-based lakehouse holding fresh business data, an MCP server that lets any agent discover and query it, and a Python runtime for the pipelines that keep it current. It works with the models you already use, so every agent you run becomes a data agent.
Who It's For
Teams evaluating, building, or operating data agents in production, including:
Data Analytics and BI teams
Building natural-language "ask the data" interfaces on top of analytical datasets.
Data Engineering and Data Platform teams
Investigating pipeline failures by correlating deployments, schema changes, data volumes, and job logs.
Customer Support and Customer Success
Assembling customer profiles from incident history, product usage, and entitlements to draft support responses.
Sales and Revenue Operations
Generating call briefs from usage data, billing records, invoices, renewal dates, and recent emails or meetings.
Product Management and Growth
Analyzing KPI changes by correlating product releases, marketing activity, and customer behavior, then drilling into the drivers with follow-up questions.
IT Operations
Correlating telemetry, incidents, and change logs to identify root causes of SLA violations.
How Tower Connects Agents to Enterprise Data
Tower provides the data layer that agents plug into, and the tooling to operate it:
- Give any MCP-capable agent access to your lakehouse through the Tower MCP server
- Serve agents fresh business data from an open, Iceberg-based lakehouse instead of stale exports
- Govern what agents can reach with isolated environments, scoped secrets, and access control
- Keep sensitive data in your own cloud or on-prem with self-hosted runners
- Deploy the Python pipelines that keep agent-facing data current, serverless or self-hosted
- Separate development, testing, and production so agents never answer from the wrong data
- Observe every run with centralized logs and metrics, and visualize tool-call dependencies
- Stay model-agnostic, from cloud-hosted 1T+ parameter LLMs to local Small Language Models
Agents answer from the same governed data your business runs on, without custom infrastructure for access, environments, or observability.
Featured Blogs
Featured Talks
Surviving the Agentic AI Hype with Small Language Models
Preparing your AI Agents for the Ice(berg) Age
Local and Serverless DeepSeek R1 on Iceberg Lakehouses
Power Your Team with Tower
Get a Python-native orchestrator of data flows and optionally use a reliable, open lakehouse built on Apache Iceberg and compatible with Snowflake, Spark, and what comes next.