N8n

Agent memory is an architecture problem, not a toggle. An ordered overview: the cognitive memory functions, why the context window is not memory, RAG through GraphRAG with their real costs, and where n8n hits its native limits.

The series finale: n8n binds to its own service on SAP BTP with a real OAuth flow. Client Credentials against the xsuaa token endpoint, a CAP service as a SAP-native stand-in, the n8n OAuth2 credential, and a workflow that enriches a ticket with a case ID from BTP.

Eleven articles covered what n8n can do. This one places where it does not belong: against SAP Cloud Integration with its EAI heritage and the Edge Integration Cell with its data boundary. With a decision tree instead of a tool takedown.

Operation goes production-ready. This article moves the single-process instance to queue mode: main accepts webhooks and enqueues executions in Redis, workers run them. With worker scaling, queue metrics, a production checklist, and a note on what queue mode does not do.

The pipeline now enriches its tickets with master data from a SAP backend. This article shows the OData binding over HTTP, SAP's own error format, graceful degradation on an unknown customer, and where AIF stays complementary.

A single AI backend degrades to the rule-based emergency exit on failure. This article builds real redundancy from two local models: round-robin in normal operation, automatic failover on a single outage, rule-based only when both are down.

Until now two separate paths ran side by side: the rule-based ingest and the AI classification. This article merges them into one pipeline, makes classification a swappable stage, and positions the rule-based classifier as a deliberate fallback behind the model.

The AI classifier runs, but what happens when the model goes down? A global error workflow with a Telegram alert, an observability stack of Prometheus, Loki and Grafana, and a silent fallback that wants to be made visible.

How an n8n workflow classifies tickets with a local language model: structured output, two backends without a cloud key, deterministic language detection, and a benchmark that exposes its own weakness.

How an n8n workflow gets an authenticated webhook endpoint, how credentials are stored securely, and how classifier logic is extracted into a sub-workflow.

Building a rule-based ticket classifier in n8n: the item model in practice, writing expressions correctly, choosing between Set and Code nodes — and where the classifier predictably fails.

Why a real ticket dataset has no place in a public repo, and how a hybrid generator using local models and Claude produces realistic test data with a reproducible distribution.

Self-hosting n8n in a production-grade setup: Docker Compose, Postgres instead of SQLite, Caddy with auto-HTTPS, persistent volumes, and the environment variables that actually matter.

What n8n actually is under the hood: editor, execution engine, task runners, workflows as JSON. Plus the item array as the central data model and where n8n fits in the integration landscape.

SAP has taken a strategic stake in n8n and embedded it in Joule Studio. Mercedes-Benz is rolling out the platform globally. A prologue to the n8n getting-started series.