Agentic workflows
Turn AI agents into governed teammates that can investigate live data, reuse trusted context, and keep monitoring the business with clear boundaries.
How teams adopt agentic workflows
Connect agents to governed context, let them investigate before acting, then schedule monitors that route findings back to teams.
Connect agents to governed context
Agents are only useful when they can reach the right data without bypassing trust. Altertable gives Claude, GPT, and custom agents governed access to live metrics, tables, and saved work.
- Start from governed access
Agent sessions inherit the same organization, environment, and role boundaries as the people who use them.
- Expose useful tools, not raw chaos
Agents get structured ways to inspect data, run queries, preview results, and suggest next steps instead of improvising against disconnected systems.
- Keep every step visible
Reads, previews, and proposed actions stay visible to the user, so automation feels inspectable rather than mysterious.
What AI and data teams get
Agents that inherit trusted data, visible guardrails, reusable context, and always-on monitoring without a separate automation data stack.
Agents inherit trusted context
Agents use the same lakehouse, metrics, permissions, and saved work as BI and SQL, so their answers start from context your team already trusts.
Bring the agents you already use
Connect Claude, GPT, internal copilots, or custom agent frameworks without rebuilding your data layer for every model or vendor.
From answer to always-on watch
When a question should keep being answered, turn the investigation into a scheduled monitor that watches the same trusted metrics.
Controls every agent inherits
Set permissions, credential access, visibility, and auditability once at the lakehouse layer, then let every agent workflow inherit the same guardrails.
Core agentic workflow capabilities
Governed agent access, responsive investigation loops, visible previews, and context that compounds over time.
Agents connect with guardrails
Claude, GPT, custom agents, and internal tools can work from scoped access and the same permissions your team already uses.
Interactive reasoning loops
Agents need to inspect, query, preview, and follow up without waiting on stale exports. Keep multi-step investigation responsive on live business context.
Preview before work spreads
Let agents preview queries, proposed insights, notifications, and follow-ups before they become shared context. Automation stays useful without becoming silent.
Context compounds over time
Saved insights, organization context, and past decisions give agents a durable memory of how your team thinks, not just a transcript to reread.
Your language, your stack
SDKs and adapters in the languages your team already uses,
so you ship without changing how you work.
Explore more use cases
Other workloads that share the same engine and governance.
Product Analytics
Capture product data through Altertable's APIs, then analyze funnels, segmentation, and behavior in the same system as revenue, operations, and warehouse data.
Analytical Database
Store data in Altertable or connect the systems you already have, then query everything through one fast SQL engine across product, business, and operational data.
Self-Serve BI
Build dashboards and visualizations on the same live engine used for SQL, then expose trusted metrics and insights to AI agents and collaboration tools through MCP.
Put agents to work on governed business context
DuckDB workers on open formats, federated SQL across your existing systems,
and an MCP server for agents — at flat monthly pricing.
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