When you build agents long enough, you hit the same wall every time: you start with one database, then add an API, then a CRM, then you need outputs like Slack, email, or Google Docs. Every new data source means new auth, SDKs, schemas, rate limits, and operational headaches—and you end up managing integrations instead of building intelligence. The funny part is this problem was solved 30 years ago. I map classic three-tier architecture (presentation, application logic, data) directly onto agent systems so you can separate concerns into a data source layer (APIs, MCP servers, RAG), a processing layer (reasoning, orchestration, tools, memory/state), and a presentation layer (Docs, Slack, email, dashboards). I break down what actually matters in each layer: freshness, auth, schemas, and reliability in layer 1; model selection, orchestration patterns, context management, memory/state, and cost in layer 2; and format, audience, timeliness, multi-channel delivery, and human-in-the-loop decisions in layer 3. Then I walk through my open-source clinical trial tracker: an MCP server wrapping clinicaltrials.gov, a Google Sheets watch list, an orchestrated monitoring pipeline that generates an HTML email digest, and optional Linear tickets plus a local dashboard. I also show where Arcade fits: it doesn’t “own” a layer—it solves authenticated tool access (OAuth) across layers so you write zero OAuth code and your agents can actually take actions reliably.