Integration Patterns

The hardest part of AI consulting is not the AI. It is the integration — connecting new capabilities to existing systems without breaking what already works. Every client has a technology landscape that predates your engagement, and your solution must coexist with it. The architecture that ignores the existing landscape is the architecture that fails in production.

At survey level, three integration patterns cover the majority of AI consulting engagements. Each one solves a different problem, carries different trade-offs, and suits a different maturity level. Understanding which pattern fits which situation is more valuable than knowing the deep implementation details of any single one.

1. RAG Pipelines (Retrieval-Augmented Generation) Connect an AI model to your own data so it can answer questions grounded in your content — not just its training data. The pattern: ingest documents, split them into chunks, embed them into vectors, store them, and retrieve the relevant chunks at query time. RAG is the most common enterprise AI pattern because it solves the most common enterprise AI problem: "we want AI that knows our stuff."
2. Agent Orchestration Multiple AI agents coordinating on a task, each with a specialized role. One agent researches, another drafts, a third reviews. The pattern: a coordinator agent receives the task, decomposes it, delegates to specialists, and synthesizes their outputs. Agent orchestration shines when the task is too complex for a single prompt but too structured for a human workflow.
3. Model Serving Deploying a trained or fine-tuned model behind an API so applications can call it in real time. The pattern: train or select a model, containerize it, deploy behind a load balancer, and expose a standardized API. Model serving is the integration pattern for when the AI capability is a feature inside a larger application, not a standalone product.