GitHub showcases Qubot, an internal Copilot-powered data analytics assistant
GitHub shared architecture insights from building Qubot, an internal agent using the Copilot harness. The helper simplifies database exploration by enabling non-technical teams to write plain English data queries.
Impact: Medium
Why it matters
You can apply this architecture to bridge the gap between technical database schemas and non-technical staff without raw access risks.
TL;DR
- 01Constraining data agents to read-only interfaces prevents unauthorized write modifications to core systems.
- 02Relying on a schema validation step before database query execution blocks invalid query payloads.
Architectural Isolation for Data Agents
Connecting a language model directly to databases presents major security challenges. To mitigate this, Qubot uses a translation architecture. The user query is passed into the agentic harness, parsed, validated against schema rules, and executed via a strictly defined read-only channel.
Model Evaluation Context
To maintain query precision across schema changes, developers should use dedicated test sets. This ensures the underlying model translates domain-specific jargon into exact table and column structures without hallucinating joins or fields. Evaluating model performance in a sandbox environment helps determine if lighter models can handle routine telemetry tasks.
Try it in 2 minutes
def run_analytics_agent(user_prompt, schema):
query = llm.generate(f"Translate to SQL using {schema}: {user_prompt}")
if validate_sql(query):
return db.execute(query)
raise ValueError("Invalid SQL generated")python
✓ When to use
- Use this approach to build query assistance tools for databases, allowing non-technical employees to read analytics safely.
- Adopt this architecture when wrapping LLMs around sensitive telemetry endpoints.
✕ When NOT to use
- Do not implement this pattern on write-heavy transaction databases where execution errors could cause data corruption.
- Do not use this for arbitrary natural language searches where structured SQL queries are not required.
What to do today
- Implement database schema schemas as structured context boundaries for analytics assistants.
- Isolate database-accessing agents with read-only credentials.
Sources