Enterprise Integration Patterns for AI-Native Architectures

The average enterprise runs 187 SaaS applications. Most of them don't talk to each other. This is both the greatest challenge and the greatest opportunity for AI-driven automation.

The Integration Paradox

Modern enterprises have more tools than ever, yet data silos are worse than a decade ago. Each department adopts best-of-breed software, creating a fragmented landscape where critical business logic lives scattered across dozens of disconnected systems.

Traditional integration approaches — point-to-point connections, batch ETL jobs, monolithic middleware — can't keep pace. Every new tool requires N-1 new connections, creating an exponential complexity curve that eventually collapses under its own weight.

Event-Driven Architecture: The Foundation

At Vistro, we've standardised on event-driven architecture (EDA) as the backbone for all enterprise integrations. Instead of systems calling each other directly, every meaningful business event is published to a central event stream. Consumers subscribe to the events they care about.

This decoupling provides three critical advantages:

• Temporal Independence: The producer doesn't need to know when — or if — the consumer processes the event. Systems can be upgraded, restarted, or replaced without breaking the chain.
• Schema Evolution: Events carry versioned schemas, allowing producers and consumers to evolve independently. New fields can be added without breaking existing consumers.
• Replay Capability: The entire history of business events is preserved, enabling new systems to "catch up" by replaying the event stream from any point in time.

The API Orchestration Layer

Not every system supports event-driven patterns natively. For legacy systems with REST or SOAP APIs, we deploy an orchestration layer that translates between synchronous API calls and asynchronous event streams. This layer handles:

• Rate limiting and throttling to respect API quotas
• Payload transformation between incompatible data formats
• Authentication lifecycle management across multiple identity providers
• Automatic retry with dead letter queuing for failed operations

Patterns We Deploy Most

Across hundreds of enterprise deployments, five integration patterns appear repeatedly:

• Saga Pattern: For distributed transactions spanning multiple services, we coordinate compensating actions to maintain consistency without distributed locks.
• CQRS (Command Query Responsibility Segregation): Separating read and write models allows us to optimise each independently — fast writes for real-time processing, rich queries for analytics.
• Strangler Fig: Gradually replacing legacy system functionality with modern services, routing traffic through a facade that progressively shifts workload.
• Anti-Corruption Layer: Isolating new domain models from legacy data structures, ensuring clean architecture boundaries while maintaining backwards compatibility.
• Outbox Pattern: Guaranteeing exactly-once event publication by writing events to a local database table before publishing, eliminating the dual-write problem.

Where AI Enters the Picture

With a clean integration layer in place, AI agents can finally operate across the full enterprise surface. An agent processing a customer complaint can query the CRM, check order status in the ERP, review shipping data from the logistics platform, and issue a refund through the payment gateway — all through a unified event-driven interface.

The integration layer becomes the agent's nervous system: it doesn't need to know the details of each downstream system, only how to publish intents and subscribe to outcomes.