Speaking as an advanced engineer, I’ll go deep into how to design systems that combine an API-first approach with event-driven architectures. This isn’t about handing out a checklist — it’s about understanding the reasoning, trade-offs and operational consequences inside real distributed software. API-first is not simply “document the API before coding.” It means the contract is the source of truth. When you define an OpenAPI spec, GraphQL schema or AsyncAPI contract before writing implementation, you immediately unlock parallel development, consumer mocking, and explicit version decisions. Teams align through contracts — not through tribal knowledge — and frontend, backend and integrations move independently. Event-driven architecture naturally complements this when you need to scale, isolate domains and accept latency and eventual consistency as facts of reality. Instead of one synchronous source of truth, you express business changes as atomic events — UserCreated, OrderConfirmed, InventoryDebited — which become the integration fabric. Each domain owns its own truth and publishes state changes declaratively. The power comes from combining the two. Synchronous contracts (HTTP or GraphQL) handle interactive operations: login, queries, immediate commands. Asynchronous events broadcast state transitions to other domains. You should treat both as first-class contracts — versioned, validated, tested — not afterthoughts. There is an API contract and an event contract. Both deserve schema rigor. From real experience: consistency is the first hard problem. Event-driven systems are eventually consistent by nature — which means UX and APIs must communicate that reality. For operations demanding immediate confirmation, use transactional outbox or change-data-capture patterns. Transactional outbox is the most battle-tested: state and outgoing event are stored atomically in the same local transaction, then a relay publishes from the outbox to the broker. No distributed transaction needed — and no risk of “state updated but event never published”. Delivery guarantees define downstream complexity. At-least-once delivery is the practical default — which requires that all consumers be idempotent. That means embedding correlation IDs, versioning entities, or deduplication logic. Always plan for dead letter queues: some messages will fail repeatedly and require human inspection. Schemas evolve. Contracts change. You need explicit policies. For HTTP APIs, semantic versioning or Accept headers work — but break clients easily. For events, use a schema registry (Avro, Protobuf, JSON Schema) and enforce backward/forward compatibility. Avoid field renames. Add optional fields. Consumer-driven contracts are excellent — consumers declare expectations, providers are validated against them in CI. Observability is non-negotiable. You need distributed tracing, structured logs and runtime metrics. You’ll soon be debugging delayed consumers, message duplication and hidden retry storms. Instrument every event: processing time, error rates, retry counts, dead letters. Propagate a correlation ID from the initial HTTP request into every event — otherwise you are blind. External integrations demand circuit breakers, exponential backoff with jitter, adaptive degradation. If a consumer gets overwhelmed, fail gracefully — do not cascade outages. Rate limiting on APIs is obvious. On event platforms, you must treat broker backpressure as first-class. Good systems fail predictably, not catastrophically. Security must apply equally to synchronous and asynchronous paths. HTTP usually gets OAuth2, JWT, scopes. Events require mTLS between services, and ideally authorization by claims or broker-level topic permissions. Encrypt data in transit. Audit event retention — immutable logs can accidentally become data leak machines. You will also face orchestration versus choreography. Central saga orchestrators offer visibility and control, but create coupling. Pure choreography gives autonomy and scale, but observability and debugging become harder. Use Sagas with compensating actions for distributed consistency without global transactions. Don’t pick blindly — match the process complexity. Testing and automation are where API-first truly shines: mockable contracts, CI contract tests. Extend that discipline to event flows: simulate broker failure, duplicate messages, replay scenarios. Contract testing across producers and consumers is essential. Pipelines should reject schema changes that break any consumer. Minimal illustration of contract duality — HTTP command and emitted event: ```yaml openapi: 3.0.3 info: title: Order Service version: "1.0.0" paths: /orders: post: summary: Create order requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/OrderCreate' responses: '202': description: Accepted for processing components: schemas: OrderCreate: type: object properties: userId: { type: string } items: type: array items: $ref: '#/components/schemas/OrderItem' OrderItem: type: object properties: sku: { type: string } quantity: { type: integer } ``` Emitted event example: ```json { "type": "OrderCreated", "id": "evt-1234", "occurredAt": "2025-10-20T12:34:56Z", "payload": { "orderId": "ord-9876", "userId": "user-42", "items": [{ "sku": "ABC", "qty": 2 }] } } ``` The final piece is organizational. API-first plus event-driven is not a technical stack — it’s a contract culture. Immutable artifacts. Discoverable catalog of events. Owner-mapped domains. Disciplined change process. Design events as business language — not database change logs. Teams that treat these contracts as interfaces evolve fast and safely. Teams that treat them as side effects produce chaos. If I had to compress it to one principle: treat contracts — both API and event — with the same gravity as your database schema. Engineer for failure from the first line. Automate schema testing ruthlessly. Pick your patterns (outbox, sagas, CDC) based on business invariants. Do that, and your system not only scales — it stays understandable and evolvable years later.