Let me be honest with you. Six months ago, our team was spending 3 full days every sprint doing something that felt important but was completely manual — discovering, mapping, and documenting database schemas for compliance reporting.
Today? AI agents do it in under 2 hours. Here’s exactly what changed.
The Problem Nobody Talks About
In banking and enterprise environments, compliance work is brutal. Before every audit cycle, someone on the team had to:
• Manually connect to dozens of databases
• Document every table, column, and relationship
• Map schemas to business logic
• Generate SQL queries for compliance reports
• Write documentation that would be outdated in 2 weeks
This wasn’t glamorous work. It was 3 days of copy-paste, context switching, and human error.
Sound familiar?
The Moment I Decided to Automate It
I remember sitting at my desk at 11 PM finishing a schema mapping document that I knew would need to be redone in 6 weeks. That was the moment I thought — this is exactly what AI agents are built for.
I had been building multi-agent systems at work using Google ADK and Gemini AI. The question wasn’t whether it was possible. It was whether I could make it reliable enough for production in a compliance environment.
Spoiler: I could.
What I Built — The db_discovery Pipeline
I built a 9-agent sequential pipeline where each agent has one job and does it perfectly.
Here’s the architecture:
Parser Agent
↓
Schema Discovery Agent
↓
Data Sampler Agent
↓
Analysis Agent
↓
Graph Builder Agent
↓
Graph Query Agent
↓
Mapper Agent
↓
SQL Generator Agent
↓
Report Generator Agent
Each agent takes input from the previous one, does its specific task, and passes results forward. Clean. Reliable. Auditable.
How Each Agent Works
Agent 1 — Parser
Reads connection configs and validates database credentials. No hardcoded secrets — everything encrypted and stored securely.
from google.adk.agents import Agent
parser_agent = Agent(
name="parser_agent",
model=Gemini(model="gemini-2.0-flash"),
instruction="""
Parse the database configuration provided.
Validate all required fields are present.
Return structured connection parameters.
""",
tools=[validate_connection, decrypt_credentials]
)
Agent 2 — Schema Discovery
Connects to the database and extracts every table, column, data type, and constraint automatically.
schema_agent = Agent(
name="schema_discovery_agent",
model=Gemini(model="gemini-2.0-flash"),
instruction="""
Connect to the database and discover all schemas.
Extract tables, columns, types, and relationships.
Return complete schema inventory.
""",
tools=[connect_database, extract_schema, get_relationships]
)
Agents 3–8
Each handles one specific task — sampling data, analyzing patterns, building knowledge graphs, querying them, mapping to business logic, and generating SQL.
Agent 9 — Report Generator
Takes everything upstream agents produced and generates a complete compliance report. Automatically. In minutes.
The Results
| Task | Before | After |
|---|---|---|
| Schema discovery | 4 hours | 8 minutes |
| Data mapping | 6 hours | 15 minutes |
| SQL generation | 4 hours | 5 minutes |
| Report writing | 10 hours | 12 minutes |
| Total | ~3 days | ~40 minutes |
That’s not an exaggeration. Those are real numbers from our production system.
The Hardest Part Nobody Warned Me About
Building the agents was honestly the easy part. The hard parts were:
LLM Timeouts
When schemas are large, Gemini would timeout mid-pipeline. Fix: pre-filter schemas using TF-IDF before sending to the LLM, so it only processes the most relevant subset.JSON Sanitization
Agents passing malformed JSON to the next agent would silently break the pipeline. Fix: strict output validation between every agent handoff.Database Auth in Enterprise
Hardcoded credentials are a compliance nightmare. Fix: encrypted connection strings stored in a secure database, fetched at runtime.
Each of these took me days to figure out. Hopefully this saves you that time.
What This Taught Me About AI Agents
The biggest lesson wasn’t technical. It was this:
AI agents aren’t magic. They’re reliable only when you treat them like production software — with error handling, validation, logging, and testing.
The teams that are winning with AI right now aren’t the ones using the fanciest models. They’re the ones building boring, reliable, well-engineered pipelines around good models.
Could You Build This?
Yes — if you have:
• Python 3.9+
• Google ADK installed
• Access to Gemini API
• A database to connect to
The pattern is simple even if the implementation takes work. Start with 2 agents. Get that working. Add more.
What’s Next For Me
I’m now extending this pipeline to handle multiple database types simultaneously and adding a natural language query interface on top — so non-technical compliance officers can ask questions in plain English and get answers.
The 3-day task is now 40 minutes. The goal is 10.
If you found this useful, follow me — I share what I’m actually building, not just what sounds impressive.