The Building Blocks of AI Automation
From automation platforms to protocols, from the knowledge layer to architecture concepts — everything that makes AI automation possible, explained simply and practically.
Türkçe sürüm: AI Otomasyon Merkezi
Trigger
AI Agent
Integration
Result
Automation platforms
n8n
Open-source workflow + AI agent platform with 700+ integrations; self-hostable.
Make
Scenario-based visual automation; strong for complex, branching flows.
Zapier
Easiest setup, 6000+ app integrations; Zapier Agents.
Connectivity & protocols
API
Connect models and systems to your app programmatically — the basis of custom agents.
Webhook
Instant, event-based trigger when something happens (new order, payment, form).
MCP (Model Context Protocol)
Open standard that connects an agent to tools and data — a common socket for the agent's hands.
Knowledge & memory
RAG (Retrieval-Augmented Generation)
Feeds the model with your own documents so answers are current, company-specific and cited.
Vector Database
Stores text as embeddings and returns the semantically closest records — the basis of RAG and agent memory.
Architecture concepts
Agentic AI
AI that understands a goal, plans, uses tools and runs the steps itself — making automation 'smart'.
Multi-Agent systems
Role-specialized agents (orchestrator, researcher, writer, reviewer) working as a team.
FAQ
- What is AI automation?
- Running repetitive business processes with AI models and automation tools, reducing human intervention. Unlike classic automation, the model can reason and decide, not just follow fixed rules.
- n8n vs Make vs Zapier?
- Zapier is easiest with the widest app support. Make excels at complex, branching scenarios. n8n is open-source, self-hostable and the most flexible for developers, with built-in AI agent capabilities.
- Can I build automation without coding?
- Yes. With no-code/low-code tools like n8n, Make and Zapier you can build automations by drag-and-drop. API, MCP and webhooks come in for more advanced scenarios.
- When do I need RAG?
- When you want the model to answer based on your own documents, product catalog or current data — with sources. It reduces hallucination and makes answers verifiable.