Five workflows, and where each one stops
Each names the problem, the sequence Backenly actually runs, what stays yours, and its known limits. A use case that cannot say where it stops is a brochure.
Developers using Claude Code, Cursor, or Codex
Give your agent a backend it cannot guess at
Your agent writes the frontend against real tables, applies schema changes through a governed kernel, and structurally cannot drop a table on a bad turn.
- Connect
- Read
- Write
- Refuse
- Type
Where it stops. MCP hosts read their manifest at process start. Tools are absent from the session that installed them until the host restarts. This is the single most common "it did not work" report, and it is not a bug.
Read the workflowTeams whose AI-built product now has real users
Your prototype has users. Nobody is operating it.
Bring an existing schema under governance (REST, policies, monitoring and an audit trail) without a rewrite and without giving up direct database access.
- Connect
- Migrate
- Adopt
- Verify
- Operate
Where it stops. Adoption is reconciliation, not a migration tool. Moving the data is your job; Backenly makes the resulting schema governed.
Read the workflowTeams on Supabase who want the operator, not a new client
Keep the client. Change what operates it.
Backenly's data plane is PostgREST, so a supabase-js frontend keeps its query grammar. What you gain is the governed change path and the autonomy loop.
- Swap
- Keep
- Move
- Gain
Where it stops. rpc() is refused. Backenly exposes no SQL functions by design, so any stored-procedure call sites need re-homing as event, cron, or HTTP functions.
Read the workflowTeams shipping products with an inference layer
Prompts are private data. Store them like it.
Conversation history, retrieval, async pipelines, and live job status, as backend features under one access model rather than four systems you operate.
- Model
- Retrieve
- Process
- Stream
- Bound
Where it stops. Realtime carries row change events, not token streams. Stream tokens from your own inference endpoint; use SSE for status transitions.
Read the workflowTeams building B2B software with shared workspaces
Tenant isolation you can demonstrate
Organizations, members, and invitations as real tables, with row access scoped by membership in the database and the boundary tested by signing in as someone else.
- Provision
- Scope
- Refine
- Prove
Where it stops. This is your application's team model. It is unrelated to Backenly account seats, which are how many people can log into your Backenly dashboard, a separate and plan-limited thing.
Read the workflow
What holds across all five
The workflows differ. These four properties do not, and they are why the workflows are possible.
One governed path for every change
Whether a change comes from you, your agent or an automated repair, it goes through the same typed kernel: validated, audited and applied all-or-nothing. There is deliberately no raw-SQL route around it.
Authorization the database enforces
Each project has its own PostgreSQL schema, and row access is decided by row-level security rather than by application filtering. A rule you cannot forget on one screen.
Evidence instead of success messages
After a build, checks run against the live runtime over real HTTP, including signing in as a second user to confirm isolation holds. Checks that cannot run report as skipped, never as passed.
A loop that keeps going after you stop
One-minute cadence on every plan, applying only reversible, snapshotted changes on its own. Auth, credentials and anything destructive wait for a human at every autonomy level.
Before you pick one
The backend operator. Your coding agent still writes the frontend and the integration code; you still make the product decisions. What the platform takes over is designing and applying schema change safely, enforcing authorization in the database, proving it works after each change, and monitoring and repairing the running backend afterwards.
An agent can write backend code well. What it cannot do is persist: the session ends and its model of your schema ends with it, and it is not watching when error rates move at 2 a.m. It also has no structural limit; nothing stops a bad turn from dropping a table. Backenly keeps the schema, the change ledger and the verification evidence, and destructive operations are absent from the agent-facing surface entirely.
When the backend is the product: a database engine, a system with microsecond latency budgets, or one whose regulation requires owning every line. Also when your team wants to own infrastructure: structure mutates only through governed actions, there is no raw-SQL path for changing it, and Backenly exposes no SQL functions, so there is no rpc() surface.
For creating backend resources, yes. MCP is the build door and there is no in-product chat builder. The dashboard is where you inspect, approve and operate. Everything else is standard: the runtime is REST over PostgREST, and you can take a direct PostgreSQL connection string for psql, an ORM or a BI tool.
One free project, no credit card
Connect your agent, build something small, and read the verification evidence yourself.