Deploy Data Agents

Run Data Pipelines

What It Is

Teams building data agents need infrastructure that lets them connect language models to reliable, up-to-date business data, iterate quickly, and deploy safely to production. Tower provides a runtime and deployment layer for data agents that works with Iceberg lakehouses and supports a wide range of models - from cloud-hosted 1T+ parameter LLMs, to locally-running Small Language Models. Tower is designed for engineering teams that want to evaluate and operate data agents without building custom infrastructure for environments, orchestration, and observability.

Who It's For

Teams that are 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 drivers through iterative “why” questions.

IT Operations

Correlating telemetry, incidents, and change logs to identify root causes of SLA violations.

How Tower helps with Data Agent Deployments

Tower provides infrastructure and tooling for developing and operating data agents:

Connect agents to fresh business data stored in Iceberg-based lakehouses

Run and compare multiple model and prompt versions in a scalable cloud environment

Separate development, testing, and production environments

Deploy agents either on Tower-managed infrastructure or into your own cloud or on-prem environment

Support local development on your own hardware, including local GPUs

Provide centralized observability (logs and metrics) across both Tower-managed and self-hosted deployments

Use code-first orchestration compatible with major agent tool calling frameworks like LangChain

Inspect and visualize tool-call dependencies to understand agent behavior and data flow

The goal is to make experimentation cheap, deployment repeatable, and production behavior observable without locking teams into a specific model or hosting setup.