Adeloop: Turning Semantic Data Models Into APIs for AI Agents

Most AI agents today can call APIs.

But very few systems solve the real problem:

how do you safely expose business data to AI agents without giving them raw database access?

That’s what we built in Adeloop.

Introducing: Adeloop Agent Console API

Adeloop can now publish semantic domains as governed APIs for external AI agents and applications.

The flow is simple:

1. Connect a warehouse, database, spreadsheet, or file source
2. Turn tables into a semantic domain
3. Publish selected domains
4. Generate an API key
5. Connect from ChatGPT, Claude, Cursor, n8n, Zapier, or your backend

The important part:

External AI agents never access raw SQL directly.

Adeloop becomes the governed execution layer between AI and data.

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Why This Matters

Most “AI data chat” products are either:

• unsafe SQL generators
• notebook wrappers
• or vector search over metadata

That breaks quickly at scale.

Instead, Adeloop uses:

question
→ semantic routing
→ metric/dimension planning
→ bounded SQL compilation
→ source pushdown execution
→ governed JSON response

This means:

• queries stay close to the warehouse
• millions of rows are not pulled into Python
• agents receive structured JSON
• governance and rate limits stay enforced

The default execution mode is:

semantic_sql_pushdown

Not Python.

Not sandbox compute.

Not “LLM writes random SQL”.

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Example

An external agent can ask:

{
  "question": "Show top customers by revenue",
  "limit": 10
}

Adeloop then:

• selects the semantic domain
• resolves semantic metrics/dimensions
• compiles safe SQL
• executes against Postgres/MySQL/Snowflake/etc
• returns answer + JSON + execution metadata

Example response:

{
  "answer": "Top result is Acme with total_revenue = 124500",
  "execution": {
    "mode": "semantic_sql_pushdown",
    "engine": "postgresql",
    "sandboxUsed": false
  }
}

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MCP + OpenAPI Support

We also added:

• MCP-compatible JSON-RPC endpoint
• OpenAPI 3.1 action schema
• API key scopes
• usage logs
• semantic metadata endpoints
• deterministic domain routing

So tools like:

• adeloopchat
• Claude tools
• Cursor
• n8n
• ChatGPT Actions

can consume governed business data without direct warehouse access.

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One Important Architecture Decision

We intentionally did NOT add E2B/sandbox execution into the main API path.

Why?

Because most business questions are:

• aggregations
• grouped metrics
• dashboards
• top-N queries
• filters
• time-series analytics

Those should execute through SQL pushdown near the data source.

Python notebooks and sandbox compute belong later as async premium analysis jobs for:

• forecasting
• anomaly detection
• ML
• simulations
• notebook/report generation

Normal analytics APIs should stay fast, deterministic, and scalable.

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The Bigger Goal

I think AI agents will need something equivalent to:

a semantic execution layer for enterprise data

Not just chat over databases.

Something that handles:

• governance
• semantic metrics
• execution planning
• safe query compilation
• federation
• caching
• observability
• API contracts for agents

That’s the direction we’re building toward with Adeloop.

Would love feedback from people building:

• AI agents
• semantic layers
• MCP tooling
• data infrastructure
• analytics engineering systems