For AI Agent Builders

Connect Your AI Agent to Your Existing Databases Using Natural Language

Hyperlambda and Magic Cloud let you turn existing SQL databases into usable AI agent capabilities. Instead of rebuilding your backend, you can describe what the agent should do in natural language and generate the APIs, SQL tools, and workflows needed to let it work with real business data.

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Connect existing SQL databases quickly

Generate database tools from natural language

Run inside a controlled backend runtime

Cartoon AI helper interacting with a large database

The problem

Most AI agents can talk about data. Fewer can safely work with it.

Many organisations already have the data they need sitting inside existing databases. CRM systems, ERP platforms, custom line-of-business applications, and legacy SQL servers already contain the records, relationships, and operational context an AI agent needs.

The hard part is not the data itself. The hard part is connecting an agent to that data in a way that is useful, fast to implement, and safe enough to trust. Most stacks still require manual integration work, custom code, and too much glue between the model and the database.

Legacy enterprise database connected to an AI agent

How Hyperlambda and Magic Cloud help

Natural language becomes database enabled backend capabilities

Hyperlambda compiles natural language into deterministic executable structures. Magic Cloud can then use those structures to generate backend endpoints, SQL wrappers, workflows, and tools that your AI agent can invoke against real databases. This gives you a direct path from description to working backend capability.

Natural language to tools

Describe what the agent should do, then generate the SQL enabled endpoints or functions needed to do it.

Existing schema to agent capability

Use the database you already have instead of moving everything into a new system or rebuilding old logic.

Controlled execution

Run generated capabilities inside a constrained backend runtime instead of handing unrestricted code execution to the model.

AI agent safely connecting to legacy systems through controlled database access

Use what you already have

Add AI on top of legacy systems instead of replacing them

Magic Cloud can wrap existing SQLite, MySQL, PostgreSQL, and MSSQL databases with generated endpoints and database aware workflows. That means your agent can work with the systems your business already runs, whether they are modern or decades old.

Instead of migrating data first, rewriting everything, or introducing a parallel platform, you can expose carefully scoped capabilities from your current schema and let your agent use them immediately.

Use cases

What database enabled agents can do

Query

Read operational data

Let agents retrieve customers, invoices, orders, leads, products, support cases, and other records from live systems.

Update

Write back to systems

Allow agents to update statuses, create records, enrich data, or trigger business actions through controlled endpoints.

Reporting

Generate SQL backed views

Create reporting tools and custom queries from natural language without building each backend path manually.

Workflows

Automate multi step tasks

Combine reads, updates, validation, and messaging into backend flows an AI agent can invoke when needed.

Legacy

Extend older systems

Expose useful database capabilities from older software without having to redesign the original application first.

Security

Keep control over access

Apply roles, endpoint boundaries, and generated backend constraints instead of giving agents direct unrestricted database access.

The difference

Most stacks generate code. Hyperlambda generates usable backend leverage.

Typical approach

Hand write integrations and SQL wrappers
Build custom glue between model and database
Rely on generated source code that still needs validation
Expose too much complexity to the application layer
Spend time rebuilding what already exists

Hyperlambda and Magic Cloud

Use natural language to describe backend intent
Generate SQL endpoints, workflows, and tools quickly
Wrap existing databases instead of replacing them
Execute inside a constrained backend runtime
Give agents practical database capabilities fast

From database to agent

From legacy database to usable agent in minutes

The workflow is simple. Connect the database, describe the capability in natural language, generate the backend tools, and let the agent start using live business data through clear boundaries.

1. Connect

Point Magic Cloud to your existing SQL database.

2. Describe

Explain in natural language what the agent should read, update, or automate.

3. Generate

Create SQL aware endpoints, workflows, or backend functions from that description.

4. Attach

Add the generated capabilities to your AI agent as tools it can call.

5. Operate

Let the agent work with real data through controlled backend access.

Ready to build

Give your AI agent access to the systems you already run

You do not need to replace your existing database to make it useful for AI. Hyperlambda and Magic Cloud let you expose the right capabilities, generate the backend tools quickly, and connect your agents to live SQL systems using natural language.

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