Direct answer
Backend engineering is the work behind a product's interface: APIs, services, databases, authentication, permissions, integrations, workflows, observability and release infrastructure. For AI-enabled software, the backend also defines tool contracts, data boundaries, approval states and audit records that decide what AI can safely do.
Backend engineering
Designing and building the server-side systems that applications rely on: business logic, data access, integration with other systems, background jobs, identity and authorization, monitoring and operational controls.
What a backend team owns
The backend is where product promises become enforceable rules.
APIs and services
Node.js services, REST and typed contracts, background workers and service boundaries.
Data systems
Relational databases, search, caching, migrations, data access rules and retention policies.
Identity and permissions
Authentication, authorization, roles, scopes and approval thresholds.
Integrations
SaaS systems, internal APIs, payment providers, CRMs, ERPs, file stores and legacy systems.
Operation
Logging, tracing, metrics, alerting, deployment, failure handling and maintenance.
Backend requirements for AI systems
AI systems need more backend discipline, not less. The backend decides what context can reach a model, what tools can be called, which actions require approval, how cost is attributed, and how a decision is reconstructed later.
Frequently asked
This is the practice the AI work is built on
We still take this work on. If the system you are describing should also be making decisions on its own, start with the AI side.