AUTOMATION
Build an Agent-Driven Data Analyst with SLayer
Enable agents to explore, query, and evolve database schemas with natural-language memory
Updated: 5/12/2026
Difficulty
medium
Time
30m
Use Case
Data analyst chatbot that learns from interactions and improves query accuracy over time
Popularity
0 views
About this automation
Use SLayer to connect an agent to a database with auto-introspected models, allowing the agent to explore data, run queries, edit measures/columns, create custom models, and save natural-language memories linked to data entities. The agent iteratively learns and makes fewer mistakes.
How to implement
1
Set up SLayer MCP server or use CLI
2
Auto-introspect your database schema for warm-start models
3
Connect agent to SLayer with MCP or Python client
4
Configure agent to explore models and run queries
5
Enable natural-language memory saving linked to models/columns
6
Let agent iterate: query → learn → refine → reuse